Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science: Recent Episodes

Felipe Flores

Artificial intelligence is a tremendously beneficial technology that's advancing at an incredibly rapid pace. As more and more organisations adopt and implement AI we find that the main challenges are not in the technology itself but in the human side, ie: the approaches, chosen problems and what's called 'the last mile', etc.

That's why Data Futurology focuses on the leadership side of AI and how to get the most value from it.

Join me, Felipe Flores, a Data Science executive with almost 20 years of experience in the space. Every week I speak with top industry leaders from around the world

In this episode of the Data Futurology podcast, where we delve into the world of Generative AI in recruitment. Our guests today are industry experts: Grant Wright, the General Manager of Marketplace and AI Products at Seek, and James Eichhorn, Principal Consultant for Data Engineering, Machine Learning, and Data Science at Talent Insights Group. Grant and James provide a wealth of insights into how Generative AI is transforming the recruitment landscape, both from a technology perspective and the human element.

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In this episode of Data Futurology, Felipe Flores and Grant Case, Regional Vice President, Head of Sales Engineering - APJ at Dataiku delve into the realm of Generative AI and its applications in the business world. They kick off by underlining the vital role Generative AI plays in organisations, and then they explore the challenges that come along with adopting this technology.

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In this informative podcast episode, Felipe Flores speaks with Jade Haar, the Head of Privacy and Data Ethics at National Australia Bank (NAB). Jade shares her inspiring journey into the field of data ethics, driven by her passion for doing right by people and contributing to the public good.

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In this episode, Kendra Vant and Tracy Moore delve into the world of generative AI and its potential for unlocking commercial value. They kick off by addressing the excitement and hype surrounding generative AI technologies and emphasise the importance of grasping the fundamentals to extract real value from these advancements.

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In this episode, host Felipe Flores interviews Alan Lowthorpe, co-founder of Adaptive Data (who advise organisations on how to accelerate the value delivered from data and AI) and James Lecoutre, Director at Talent Insights Group as they delve into the world of data analytics and AI leadership, sharing insights on building successful teams, embracing diversity, fostering a growth mindset and navigating challenges in data analytics and AI.

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This week on the Data Futurology podcast, we host Chad Sanderson, the Chief Operator of Data Quality Camp.

Over the ten years Sanderson has been involved in data, he has held key roles in companies including Convoy, a late-stage freight technology company, and Microsoft, where he worked on the AI platform team.

Sanderson’s experience with these companies made him realise that there was a need for a platform where data specialists could come together and discuss strategies for maintaining high-quality data in their organisations.

His group, Data Quality Camp, has since attracted nearly 8,000 members, and has become a real meeting place to discuss everything from the technical implementation of a data strategy, through to helping members find work in an increasingly dynamic and disrupted workplace environment.

On the podcast, Sanderson highlights the strategies he has seen to deliver high-quality data environments, some of the traps and pitfalls to avoid, and how data specialists can better engage with and gain buy-in from the other lines of business within the organisation.

For insights direct from someone at the heart of the data quality conversation, don’t miss this in-depth conversation with Chad Sanderson.

Join the Data Quality Camp on Slack (https://dataquality.camp/slack)

Connect with Chad: https://www.linkedin.com/in/chad-sanderson/

Thank you to our sponsor, Talent Insights Group!

Join us for our next events: Advancing AI and Data Engineering Sydney (5-7 September) and OpsWorld: Deploying Data & ML Products (Melbourne, 24-25 October): https://www.datafuturology.com/events

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At Data Futurology’s OpsWorld conference in March, a panel of experts came together to discuss the importance of getting measurements, processes and methodologies right to drive DataOps and MLOps across the organisation.

The panel consisted of Katherine Fowler, Head of Business Transformation at L’Occitane Australia, Amar Poddatooru, Head of Data and Technology at Australian Ethical, and Emyr James, Head of Data at Resolution Life and moderating the discussion was Andrew Aho, Regional Director, Data Platforms at InterSystems. It became a far-reaching discussion that started with methods to define and measure the ROI of data and analytics initiatives and how to get those projects off the ground. The discussion moved on to overhyped technologies in the data space, and then looked forward to what is on the horizon for the years ahead.

As the panel discussed, there is a lot of interest among consumers in some innovative technologies, including ChatGPT. This is in turn driving a lot of interest at the executive level at rolling out solutions that use these tools. However, without the right foundations in place, and without proper concern for the privacy and regulatory risks associated with these tools, they will cause the data team more headaches than they’re worth.

This panel discussion is essential for understanding how to structure a foundation for data success, be disciplined in deploying the available resources across the data team, gain executive buy-in, and then steadily build the practice up.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Join us for our next events: Advancing AI and Data Engineering Sydney (5-7 September) and OpsWorld: Deploying Data & ML Products (Melbourne, 24-25 October): https://www.datafuturology.com/events

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

What we discussed

2:07: Felipe introduces the Measurements Thought Leaders panel and moderator, Andrew Aho.

3:48: How do you define and measure data and analytics ROI?

7:21: A discussion on metrics that help get data initiatives off the ground.

9:41: How a data leader needs to focus on the data platform, and articulate both the “big picture” view and the details.

12:35: As more organisations adopt ops, processes and methodologies, what challenges might people anticipate arising, and how can those be addressed?

17:24: What can data professionals do to help solve the change management challenge?

18:34: What are the challenges and impact of upcoming “silver bullet” technologies like ChatGPT?

20:16: What is currently overhyped in the data space (and why)?

24:03: What can we as data scientists do to ensure that we’re looking at the right risks and drawing accurate conclusions on what is right for the business?

26:13: If the goal is to focus on data science, how can we also keep experimentation and creativity going?

29:49: How do you estimate the value of change to get executive buy-in?

31:18: What upcoming developments and trends will emerge over the next five to ten years?

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This week on the Data Futurology podcast, we welcome Orla Glynn, Executive – AI, Reporting, Insights and Automation Configuration at Telstra. Glynn leads one of the biggest groups of data specialists to drive innovative AI and analytics across the company.

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At the recent Advancing AI event in Melbourne, we were privileged to have a presentation by Vinay Joseph, the Pre-Sales Lead for IDOL at OpenText in APAC.

Vinay gives an overview of the features of IDOL and how they can help data science teams bring automation and AI to the use of unstructured data. He presents a wide range of case studies and use cases. These include how law enforcement and the military, right through to news organisations and political campaigns might be able to use the data to draw real-time and in-depth insights that would otherwise be inaccessible.

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This week on the Data Futurology podcast we host Paul Milinkovic, the APAC Regional Director for the leading data integration platform, StreamSets. Milinkovic joins us to share his insights into data engineers' challenges and the pipelines they manage and maintain.

One statistic really highlights just how challenging work environments have become for data engineers: 76 per cent of organisations have a pipeline break at least monthly and for 36 per cent, it's weekly. Rather than contributing strategically to their organisations, engineers split their time between diagnosis and repair, and building new pipelines. This costs the organisation, as half the time the engineer isn’t being used strategically. It also leads to cultures of over-working, burnout, and high levels of churn within the data engineering team.

Another challenge data teams struggle with is competing priorities. When multiple lines of business need pipelines developed, teams often need to triage to accommodate priority tasks, and this affects overall company outcomes. Being able to help organisations deliver a low or no-code environment that is highly visual and accessible to non-data specialists has been a critical benefit for organisations that have adopted StreamSets.

Milinkovic then shares two case studies where StreamSets has helped with overcoming these challenges. In one, a bank achieved a seemingly impossible task – becoming compliant with looming Consumer Data Act requirements within four months. Then, a second bank was able to leverage StreamSets to its data to detect and thwart $9 million in fraudulent activity in a single month.

For more deep insights into overcoming the challenges facing modern data engineering teams, tune into the podcast!

Links

Website: https://streamsets.com

Follow on LinkedIn: https://www.linkedin.com/company/streamsets/

Whitepapers:

https://go.streamsets.com/Whitepaper-Dollars_and_Sense_UGLP.html?utm_medium=website&utm_source=DataFuturology&utm_campaign=eg_dollars_and_sense_of_dataops

https://go.streamsets.com/Whitepaper-Dollars_and_Sense_UGLP.html?utm_medium=website&utm_source=DataFuturology&utm_campaign=eg_dollars_and_sense_of_dataops

https://go.streamsets.com/230214-lifting-the-lid-on-data-integration-UGLP.html?utm_me[…]turology&utm_campaign=eg_lifting_the_lid_on_data_integration

What we discussed:

00:00 Introduction

02:22: Felipe introduces Paul Milinkovic.

03:38: Milinkovic shares his background and his history with data at various levels and applications.

06:04: Milinkovic overviews StreamSets – when and why the company was founded, and what its core capabilities are.

09:04: What are the main issues that StreamSets helps data engineering teams solve?

12:57: How does StreamSets address traditional data pipeline design and build challenges?

12:33: What are the benefits of having a solution that is visual and accessible to non-technical users?

22:51: One of the common questions with the self-service approach to data is governance. How can that be handled while still allowing full flexibility?

26:46: Data engineers care a great deal about the quality and accuracy of data and the platforms that it sits on. Milinkovic explains why it is so important that they have the tools to be able to deliver that to the organisation.

31:24: What is the financial impact of data engineering teams spending as much time fixing pipelines as they are?

33:49: Milinkovic shares some case studies and use cases to highlight the value of StreamSets’ approach to data engineering.

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At our recent Advancing AI Melbourne event, Jonas Christensen, formerly Head of Data Science at Maurice Blackburn Lawyers, hosted a lively and insightful panel discussion featuring three prominent leaders in data and AI:

· Christine Smyth, Chief Strategy Officer, Defence Health

· Dr Michelle-Joy Low, Head of Data & AI, Reece Group

· Nonna Milmeister, Chief Data and Analytics Officer, RMIT University

The panellists emphasise the importance of building a culture that embraces AI and data-driven insights. Dr. Christine Smyth highlights the need for cooperation within the organisation, involving data students and building cross-functional teams with their technology counterparts. Christine also emphasises the significance of building trust in AI by being transparent about biases and addressing legitimate concerns. In order to combat fear and misunderstanding, increasing data literacy across the entire organisation is crucial.

In a data context, a significant amount of effort goes into developing communication structures and accountability frameworks. These structures enable all teams involved to effectively communicate their contributions towards delivering tangible business value. However, this process is an ongoing journey, especially as organisations evolve and grow. Dr. Michelle-Joy Low highlights the importance of establishing a common language and effective communication channels within data teams. By doing so, organisations can foster collaboration, enhance accountability, and ultimately deliver value through their data initiatives. Whilst this endeavour may require continuous effort and adaptation, it is a vital discipline that directly contributes to the success of data-driven organisations.

This episode also reveals insights from Nonna Milmeister who believes that to achieve success as data leaders, cooperation is key. Building strong collaboration with every part of the organisation is absolutely essential. Only by being transparent about biases and addressing them head-on, trust can be established. Trust leading to firm foundations that will foster successful data impact and outcomes.

People often have concerns about AI replacing their jobs entirely, but here's an interesting stat: according to the World Economic Forum, while 85 million jobs may be replaced by 2025, a staggering 97 million new jobs will be created. So, instead of fearing job displacement, our role as data leaders should focus on increasing data literacy within our organisations. As the role of the data leader evolves our mindsets and approaches need to also.

This is an insightful and important podcast for anyone interested in learning how organisations can build effective, productive, and innovative teams around data.

Thank you to our sponsor, Talent Insights Group!

Join us for our next events, Data Engineering and Advancing AI Sydney (5-7 September): https://www.datafuturology.com/events

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In this episode, Alex Jenkins, Director at WA Data Science Innovation Hub, discusses the potential of AI advancements in revolutionising the education system. Jenkins envisions a future where education moves away from the one-size-fits-all approach and embraces a mastery model, allowing students to progress at their own pace and ensuring complete understanding before moving on to the next topic. The use of AI as virtual educational assistants can provide personalised tutoring, benefiting students by improving their educational outcomes. Studies have shown that one-on-one tutoring can significantly elevate students' performance.

Large language models, such as AI assistants, can be tailored to individual students' learning styles and strengths. This personalisation can enhance critical thinking skills, broaden students' worldview, and help them make informed decisions about their academic journey. By leveraging AI, teachers can manage classrooms with the assistance of virtual teaching aides, enabling each student to master the material before progressing to the next level.

Looking ahead to the next twelve months, Jenkins anticipates the transition to a mastery model of education, especially in STEM subjects like mathematics. This approach will ensure students achieve true mastery of concepts before moving forward. Furthermore, AI technology can enhance teacher productivity by providing resources, such as lesson plans and tailored exercises, that cater to individual students' skill levels. Khan Academy's Carmego AI serves as a leading example in this field, offering personalised tutoring and empowering teachers with effective teaching tools.

Jenkins acknowledges the importance of considering the practical implementation of AI in education. While the technology holds immense potential, it should not replace socialisation, interaction, and hands-on learning in the classroom.

While concerns about hallucinations and AI-generated errors exist, Jenkins believes these risks are manageable and can be minimised through guided use cases and ongoing improvements in technology. He compares the trajectory of large language models to the development of space travel, where initial imperfections and limitations pave the way for future advancements and increased reliability.

Reflecting on his personal journey in technology and data science, Jenkins emphasises the importance of promoting AI and data science education. He focuses on stimulating demand for AI services, fostering collaboration between academia, public services, and private industry, and encouraging students to pursue data science as a career path. Through initiatives like hackathons, the potential of AI in areas like emergency services becomes evident, showcasing how technology can save lives.

Lastly, Jenkins discusses the upcoming Data & AI for Business Conference & Exhibition, scheduled to take place in August in Western Australia. The conference aims to explore the potential of data analytics and artificial intelligence in transforming businesses. It welcomes participants regardless of their AI or data backgrounds, as the focus is on understanding how these technologies can drive business growth and change.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Visit the WA Data Science Innovation Hub https://wadsih.org.au/

Learn more about the Data & AI for Business Conference & Exhibition 2nd & 3rd August: https://wadsih.org.au/conference/

Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events

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In this episode, we explore an engaging talk given by Niall Keating, General Manager for Technology Data Platforms at Sportsbet, during his recent appearance at the Data Engineering Summit in Melbourne.

Niall generously imparts invaluable insights on the journey of cultivating a data culture that yields long-lasting business impact. Throughout the conversation, Niall showcases tangible examples of how Sportsbet has effectively utilised data and technology to drive innovation and elevate customer experiences. Sportsbet, Australia's largest online bookmaker, faces unique challenges due to the dynamic nature of their product, where prices constantly change.

To overcome these challenges, Sportsbet has invested significantly in technology and data infrastructure. One use case Niall highlights is their adoption of machine learning, with over 20 models currently in production. These models are employed to extract actionable insights, enabling Sportsbet to make data-driven decisions and enhance their offerings.

Niall emphasises the importance of establishing a solid foundation in data culture and leveraging data for decision-making and financial reporting. He provides a specific use case of how Sportsbet utilises quantitative analytics to calculate probabilities and set prices for their core product. By harnessing data and analytics, Sportsbet optimises generosity, personalised experiences, and aims to provide the best value to their customers.

Another use case Niall discusses is the application of data in safer gambling. Sportsbet is committed to making gambling safer, and they leverage data to identify potentially risky behaviours and intervene when necessary. Niall highlights the journey Sportsbet has undertaken over the past five years in building effective data products to promote safer gambling practices.

When it comes to sustainability in data, Niall shares three educational stories that provide valuable insights. In one use case, he emphasises the importance of avoiding quick wins and taking an iterative approach aligned with strategic goals. He discusses the challenges involved in transitioning from human to AI automated decisions and the need to bridge the gap effectively.

Lastly, Niall shares a use case centred around Sportsbet's product journey in safer gambling. He highlights the time and collaboration required to build effective data products that prioritise customer safety. This use case demonstrates the impact that data-driven approaches can have in creating a safer gambling environment. By adopting a long-term perspective and focusing on values such as safer gambling and customer-centricity, Sportsbet sets an example of how data culture can drive innovation and create positive outcomes.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events

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Topics Discussed:

02:39. Introduction to Niall Keating and his background in software engineering.

04:08 Overview of Sportsbet as Australia's largest online bookmaker, serving one million active customers.

05:04 Investments in technology and data infrastructure, with a focus on machine learning and the impact of over 20 models in production.

07:06 The importance of getting the basics right in data-driven decision-making, financial reporting, and core product development.

09:14 The journey towards sustainability, including the focus on personalization, safer gambling, and aligning products with the company's vision and mission.

15:37 The challenges and lessons learned in evolving the data platform, including the adoption of lake house architecture and partnerships with AWS and Databricks.

22:04 The importance of building data products over time, collaboration between data science and analytics teams

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This week on the Data Futurology podcast, we have an in-depth conversation with Conor O’Neill, the Head of Data Science at Compare The Market exploring his career journey and current role leveraging data and innovating with machine learning.

When O’Neill landed at Compare The Market, he quickly found himself in a senior data role within an organisation that needed to both transform and mature its approach to data. On the podcast, O’Neill walks through the various stages of transformation, and getting the rest of the organisation aligned with that vision.

He also shares some use cases that Compare The Market is effectively leveraging data for, as well as how they have been building ML products. He explains how he involves data scientists in this process and offers advice on building ML as a product when it comes to planning, delivery and infrastructure.

Finally, O’Neill shares some thoughts on the difference between a data scientist’s role and that of a senior manager, and how this shifts the perspective and how a data professional will look at projects. He then rounds out the conversation with some thoughts about where data science is heading as a profession.

For anyone interested in data science, O’Neill’s unconventional journey into and through the profession is both interesting and inspiring. Enjoy the show!

Connect with Conor: https://www.linkedin.com/in/conoroneill1/

Thank you to our sponsor, Talent Insights Group!

Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September):

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What we discussed

2:26: Felipe introduces Conor O’Neill.

3:23: O’Neill shares his journey from astrophysics to data science.

6:49: In astrophysics, the data sets that scientists work on are massive. O’Neill shares some insights about how he managed data in that role.

8:40: O’Neill shares his journey at Compare The Market so far.

12:04: O’Neill shares some information about a current data project that he and his team are working on.

18:08: Compare The Market had to do significant foundational work in transformation. O’Neill shares insights into that process.

21:18: O’Neill shares his experience in getting the Compare The Market organisation aligned behind their data vision.

25:12: O’Neill explains the value of having data scientists involved at the earliest stages of transformation design.

28:44: O’Neill describes his experience in moving from a data scientist role to heading a team, and the differences between these roles.

32:56: O’Neill explains some of the thinking that goes into reusing data projects, as well as how they decide the projects to not follow through.

34:04: Getting a model in front of the end users and driving adoption is a critical step – O’Neill explains how he has approached it for Compare The Market.

37:54: O’Neill overviews the various consumers of the work done by the data team, and how the data team needs to think about each of them.

40:51: Tips and guidance for creating ML as a product to be consumed internally

45:48: O’Neill shares some thoughts on how the data science industry is evolving.

Key Quotes

“We’ve been on a transformational journey now for a little over a year, and that’s been really good. We’ve been migrating off our legacy on-prem stack to Databricks. We’ve also been focused on getting the right people, and then also establishing a process, because if you just change the tool, you haven't fixed the issues, typically.”
“You don't want your control group to be too large and you then miss opportunities. But you also don't want it to be so small that you don't get sufficient data. That's where the algorithm behind our recommendation system controls that, to optimise according to our confidence that we are or are not exceeding the required threshold, and adjust the weighting of the control group accordingly.”

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This week we bring you a special episode of the Data Futurology podcast, featuring the keynote panel from our OpsWorld conference earlier this year featuring guests at different levels of data maturity. They shared their stories of the journey to enabling and unlocking the true value of data self-service..

The panel featured Kate Carruthers, Chief Data & Insights Officer, UNSW Sydney. She shared the university's experience, which has had a mature data environment for several years. At the other end of the table was Conor O'Neill, Head of Data Science, Compare The Market. He represented an organisation that is rapidly addressing a lack of data maturity across the organisation.

The third person on the panel was Arvee Manaog, Head of Enterprise Systems, Data & Information Management, and Integration, EG Australia. She shared insights on how to effectively get organisation-wide buy-in, and then effectively educate all stakeholders on how to effectively use self-service.

The panel was wide-ranging, starting off with a discussion around best practices in data self-service, before moving on to an in-depth summary of how to effectively approach self-service from each level of data maturity.

There was also a robust Q & A session at the end of the panel. Through the robust audience questions, the panellists discussed strategies for ensuring data trustworthiness in self-service. They also discussed how ROI is best measured with self-service data practices.

Businesses of all sizes that want to maximise data value should look at effective self-service approaches. This panel provides invaluable insights into both getting started and continuing to innovate once the data environment has been fully modernised and transformed.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September):

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What we discussed:

2:07: Felipe introduces the three panelists.

3:19: Carruthers explains UNSW’s perspective around best practices in data self-service.

6:23: Manaog explains the challenges of secure self-service in EG Australia.

10:38: Manaog explains the initial steps EG Australia took to get started on the data self-service journey.

14:40: O’Neill describes some self-service approaches he's seen work well.

19:50: Carruthers describes how UNSW has kept engagement with DevOps-created dashboards and models high across the organisation.

22:50: The panel takes audience questions, with the first being “How do we influence and motivate data silo owners to share for indirect enterprise outcomes?”

27:07: How can a mature data organisation bring together data literacy and digital literacy across users?

28:11: For a less mature data organisation, how can data leads ensure data trustworthiness in self-service?

30:14: There are trade-offs involved in self-service models. How can those be managed in the pursuit of a self-service culture?

35:38: What are the most effective techniques for measuring ROI with self-service data practices?

Key quotes:

Manaog: “We’re using DataIQ. And it actually helps because it's easier for users. I got a good adoption rate for that because it’s possible to do drag and drop, there are recipes and users don't need to code. They can easily do their analysis, create their workflows and then come to the hub and say, can you productionise this?”
O’Neill: “In one model, we're doing a hub and spoke approach, where we have champions placed within the business units. We are working with those champions to ensure that we understand how they're using the report. It’s not just what they want to see. But in practice, what are they doing with it?”

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In Part 2 of the Leaders of Analytics podcast that was recorded last year with host, Jonas Christensen, Felipe discusses Honeysuckle Health and what he has done at this exciting, innovative company.

Felipe found the perfect home for his ambitions and interest in data at Honeysuckle Health. He was one of the first to join the company a few years ago, and right from the start, data, analytics and AI have been the driving force behind the business.

What’s more, all of that data and analytics are being used in a way that furthers patient outcomes. Felipe had previously had years of experience in the financial services sector, and while the advanced use of data there was an interesting challenge, he wanted to do something that would result in more positive outcomes for people. As coincidence would have it, Honeysuckle Health was looking for a data specialist at the exact time Felipe was looking for his next role. The rest, as they say, is history.

After describing the background and goals of Honeysuckle Health, Felipe then spends the rest of the podcast discussing the way Honeysuckle Health gathers data and gets the support of professionals in the health industry. He also talks about the ethical implications and the challenges of undertaking data methods that are standardised in other sectors. This includes addressing how to engage in experimentation with data in healthcare when the stakes are so high.

Tune in to the full and in-depth podcast, and get some great insights into the role that data will play in healthcare, now and into the future!

Thank you to our sponsor, Talent Insights Group!

Listen to the Leaders of Analytics Podcast: https://www.leadersofanalytics.com/

Join us for our next events Advancing AI and Data Engineering Sydney:

https://www.datafuturology.com/events

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What We Discussed

2:40 Felipe explains his role at Honeysuckle Health and what his day-to-day role looks like.

9:39 Felipe breaks down how Honeysuckle Health leverages data to improve healthcare outcomes and better engage the health industry.

15:07 Jonas asks Felipe where Honeysuckle Health gets its data from, and how the team interacts with the frontline professionals around data.

23:34 Jonas asks Felipe to describe the structures of Honeysuckle Health, and the financial, technological and IP “firepower” that sits behind it.

28:05 Felipe is asked to think ahead and describe where we’re going to be using data to improve health care and society.

35:14 Felipe discusses experimentation in health care – experimentation is essential in determining what works and doesn’t work, but the stakes are entirely different to, say, advertising.

Key Quotes

“Before working in Honeysuckle Health, I'd been in banking and finance for about five years. I found the challenges super interesting, and the applications for AI were almost endless. I think banking and finance are a little ahead of other sectors in embracing this too. But the whole time that I was there, I felt like we were using this amazing technology to sell people money. I was enjoying the technical side, but over time, I wanted to move into something different, something that ideally was more purpose-driven.”
“One of the beautiful things about working in data science is that you can move across industries quite freely.”
“Our mission is to help people live healthier lives, the way that we're doing that is through data science. We’re taking the playbook of the big tech companies in the US and what they did to advertising, and applying it to healthcare, for good outcomes. What I mean by that is that we take key aspects of personalisation, and the ability for data to help us find people at the right time, and offer them a message that will motivate them to actions like developing better habits or preventively seeking treatments.”

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This episode of the Data Futurology podcast is actually the reverse of normal – most of the time Felipe interviews experts in data science, but this time it’s his turn to be interviewed! Last year, he was on Jonas Christensen’s excellent Leaders of Analytics podcast, and we’ve got permission to republish it here.

In the wide-ranging interview, Felipe starts by describing his history. If you haven’t heard the story before, it begins with Felipe growing up in the driest parts of Chile. It then continues with him teaching himself databases in his first job in IT, after originally coming to Australia as a backpacker with very basic English. From there Felipe's career in data has taken off, both with his roles in financial services and healthcare, and the launch of Data Futurology.

Deeper into the interview, Felipe describes the goals behind the podcast and the events that Data Futurology runs. He then ends the conversation with some insights about how data currently works in organisations, and what the future may hold.

One of the most interesting things that Felipe has observed over the years is the potential for data specialists to “graduate” to the most senior roles in organisations. Just as CIOs moved from being a relatively isolated part of the business with few prospects to now being seen as prime candidates for CEO roles, the head of data analytics will increasingly be called on to show broader leadership within their organisations.

What data professionals need to do is step up their “soft” or “power” skills (depending on which term you want to use), Felipe says on the podcast. One of the driving goals of Data Futurology is to help data specialists identify these opportunities within themselves and then work on them.

To get a real sense of just how passionate Felipe is about data and the people that work in this space, his appearance on the Leaders of Analytics is a must-listen.

Thank you to our sponsor, Talent Insights Group!

Listen to the Leaders of Analytics Podcast: https://www.leadersofanalytics.com/

Join us for our next events Advancing AI and Data Engineering Sydney:

https://www.datafuturology.com/events

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

What We Discussed

00:00 Intro to Leaders of Analytics

2:30 Jonas Christensen introduces Felipe to his audience.

4:42 Filipe explains his background and history with data science.

14:01 Jonas asks what is unique about Felipe’s career, across all his self-taught knowledge and entrepreneurship?

19:00 Jonas asks what encouraged Felipe to start Data Futurology, and how he got it started.

25:54 Felipe shares his long-term vision for what Data Futurology could turn into.

28:37 Felipe shares his views on what the big trends in data science are.

37:10 Felipe discusses the implications of data science being a relatively new area of specialisation, in the context of the business as a whole.

40:15 Felipe shares some great examples of data analytics being used in a creative, innovative and high-impact manner by companies.

44:15 Felipe shares his vision of what the perfect data-driven organisation would look like and how it would handle data, analytics, and AI

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This special episode was recorded LIVE and in-person with Brian Ferris, Chief Data, Analytics and Technology Officer at Loyalty New Zealand. He shares on how to get value from your AI investment and how to look at the interplay and relationship between data leaders and the senior executive team.

Brian stresses the importance of aligning with execs on the business strategy first, then working backwards to your AI strategy. According to Brian, the first step is for the data leaders themselves to shift their mindset from being an expert in their field, to instead become an enterprise leader. This means developing the capacity to have a conversation with other stakeholders within the organisation on their terms and understand what keeps them up at night. It also means looking at decisions through the lens of what is good for the overall business.

Brian and Felipe also share key steps in nurturing talent to take on leadership roles. It’s imperative to create a culture of psychological safety within the organisation and identify when an individual is ready to start taking on a leadership role and equipping them with enterprise skills. It also means helping them transition beyond looking at the data to their broader role within the organisation.

Finally, Felipe and Brian discuss why data leaders need to leave their egos at the door, and not become emotionally invested in or defensive of projects. The data leader should be one of the leading voices within the organisation, but to get there, a collaborative spirit and a goal to take actions that are beneficial to the organisation are key.

In this interview, Ferris dives deep into all these topics. He offers insights according to his own approach to the subject, and challenges some of the conventions we take for granted. Tune in to learn more!

Thank you to our sponsor Talent Insights Group!

Connect with Brian: https://www.linkedin.com/in/brian-ferris-a053532/

Join us at one of our next events!

Data Engineering Summit Sydney:https://www.datafuturology.com/data-engineering-summit-sydney-2023

Advancing AI Sydney: https://www.datafuturology.com/advancing-ai-sydney-2023

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

WHAT WE DISCUSSED

00:00: Introduction.

2:05: Felipe introduces Brian Ferris.

2:34: How to get value from your AI investment.

8:29: The value of collaborative approaches within organisations – how can the data team drive this?

13:09: If the data team needs to both support the organisation and lead it, how does it balance those priorities?

18:14: How can a data professional bridge the gap between being a subject matter expert to having a broader understanding of the business?

22:56: Talking about soft influence – what can people do on a peer-to-peer level to build influence within an organisation?

28:47: Why it’s critical to shift thinking away from “being right” and “winning”.

33:15: What are some of the most effective techniques for creating psychological safety between peers?

36:07: What can data leaders do to incentivise adoption across the organisation?

38:58: Why proof-of-concepts are not always the appropriate way to go (and the limited circumstances under which they should be tried).

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This week we welcome to the podcast, Joanna Marsh, the General Manager of Innovation and Advanced Analytics for Investa Property Group. She’s also the CEO and Co-Founder of a “side hustle” at Exomnia, a startup that provides real estate companies with a modular approach to analytics.

Exomnia has only been in operation for four months, but it is already turning heads. It has recently completed a pre-seed funding round for an impressive $1.5 million. On the podcast, Joanna shares some deep insights into the opportunity and challenges of building a data startup.

Data startups need to meet cyber security expectations before they can begin interacting with enterprises around data. The enterprises have strict regulatory requirements in this area. This creates a challenge for the startup, as they need to invest in gaining certifications before they can even build the MVP that most pre-launch startups focus on.

However, the gap in the market is significant, and as Joanna says, Exomnia is already resonating with foundation clients. With advanced analytics available at the click of the button, Exomnia is poised to make some real waves in the property technology space.

Tune in to this podcast for some fascinating insights on building a data company at its earliest stages!

Thank you to our sponsor Talent Insights Group!

Connect with Joanna: https://www.linkedin.com/in/joannamaemarsh/

Join us for our next event Advancing AI Melbourne https://www.datafuturology.com/advancing-ai-melbourne

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

What we discussed

9:59: Felipe introduces Joanna, and then asks to overview her career to date.

15:11: How long did Joanna have the idea for Exomnia before pulling the trigger?

24:22: Joanna explains the challenges that she faces in protecting her IP when starting up a data company.

26:34: How was Joanna able to navigate challenging discussions with her first investors?

32:52: How has Joanna avoided conflicts of interest in the first investors and foundational customers being the same?

35:21: One of the biggest challenges for startups when working with corporates is managing all the requirements and processes around insurance, security and privacy that they need to meet. Joanna overviews how her company went about this.

41:49: Joanna explains the value of using open source so other startups can “plug in” to Exomnia’s data and platform.

44:29: Joanna and Felipe compare the challenges of managing different kinds of data, based on how sensitive the sector is towards data.

47:24: What’s next, as Exomnia continues to build up as a startup?

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In the world of data analytics, there are few that have achieved as much as Bora Arslan, who joined us for this week’s podcast. Arslan has driven data transformation exercises across some of the largest organisations in the world. These organisations include Walmart and Ford in the US, and IAG here in Australia.

On the podcast, Arslan shares many insights from his time as a Chief Data Officer. From his strategies for getting organisational buy-in for transformation, to the ways in which he prefers to build and manage teams, Arslan provides us with a blueprint for how the modern data executive should look at the work that they do.

One of the key messages that Arslan shares is that data analytics executives need to get as close to the organisation as possible. If they report to the CIO and their team is nested within IT, they’ll be seen as a support function, rather than a strategic one. The closer the Chief Data Officer can get to other lines of business and the CEO, the better they can understand the needs of the business and develop strategic and transformative solutions in direct collaboration with the other key stakeholders.

The challenge is that to be able to do this, the data team needs to learn how to speak the language of the other executives and lines of business. This has been one of the key reasons for Arslan’s ongoing success in his own roles.

Tune in to hear more great insights from one of the real thought leaders in our space!

Thank you to our sponsor, Talent Insights Group!

Connect with Bora: https://www.linkedin.com/in/bora-arslan/

Hear more from Bora and our awesome speaker faculty at Advancing AI Melbourne: https://www.datafuturology.com/advancing-ai-melbourne

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

What we discussed

0:00 - Introduction

3:45 – Bora explains his background and what the last eight years in various executive roles has been like.

8:36 – How to define the role of a chief data officer in a large enterprise?

13:13 – What leaders can do to lead change management across the organisation and bring people on the transformation journey.

15:58 – How data analytics heads benefit from direct interaction with the CEO and executive team, rather than being a support function to the CIO.

18:46 – The most effective ways Chief Data Officers can influence C-level executives around them.

21:21 – On building teams: What are the most effective ways to structure data teams?

23:29 – The most effective ways to optimise project delivery, and the value of having a project management team within the organisation.

26:30 – Should the change management function sit within the data analytics team, or should it be more centralised within the business lines?

28:03 – A summary of the processes and methodologies key to driving successful analytical functions.

32:40 – Looking forward: The technologies to look forward to in the next year or two.

35:57 – Bora shares his career highlights to date.

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This week in the Data Futurology podcast, we have a special presentation to share. Ben Taylor, the Assistant Commissioner for Data Insights at the ATO was one of the leading keynote speakers at our recent OpsWorld event in Sydney. There, he provided delegates with a deep dive into the data journey for the ATO in recent years.

As you can probably guess, the ATO handles millions of lines of data every day, across data lakes that are petabytes in size. With a data team of around 800, there is often the sense that they’re racing against chaos to deliver. However, in recent years, the effort to transform and modernise the approach to data has been highly successful. The ATO was able to transition to cloud-driven data systems, and is now seen as a deep and strategic partner to the other lines of business within the organisation.

In this presentation, Taylor shares some open and transparent examples of the challenges that the ATO faced, the steps that they took to embrace AI and automated analytics while maintaining human oversight and decision-making, and how the data team went about building trust to earn the support of the other lines of business.

He also overviews the value of XOps – what that means from the ATO’s perspective – and why all data leaders should be looking at defining and adopting a XOps approach to their own data strategy.

For deep insights into one of the largest data-driven organisations in Australia, Taylor’s presentation on the ATO’s experience with data is essential.

Connect with Ben Taylor: https://www.linkedin.com/in/ben-taylor-962a2a60/?originalSubdomain=au

Joins us for our next event Advancing AI Melbourne https://www.datafuturology.com/advancing-ai-melbourne

Join our Slack Community:https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

WHAT WE DISCUSSED

2:17: Introduction to Ben Taylor’s presentation.

7:06: Taylor introduces himself and provides the historical context for the ATO’s approach to analytics.

12:53: Taylor describes the state of the ATO’s data environment eight years ago, and overviews the transformation project to modernise it.

14:24: Taylor describes some of the challenges that the ATO found with centralising the data and analytics function.

16:42: XOps in the ATO – what challenges led to the ATO approaching data this way, and what impact did it have?

18:03: What, exactly, does “XOps” mean to the ATO? Ben shares his insights on the conversation.

19:27: Taylor shares an example of what XOps looks like in action at the ATO.

23:45: How did the ATO avoid becoming too process heavy, as Government agencies can at times become?

27:13: How can teams handle the sense of “chaos” that comes from increasing demands from lines of business, while also managing the legacy tech debt?

29:13: Q & A with Taylor from the audience.

EPISODE HIGHLIGHTS

“Since the earliest examples of tabular data structures two and a half 1000 years ago to the earliest examples of statistical data and analytics about 350 years ago, the pace of human abilities in regards to data analysis has increased at an incredibly, incredibly fast pace.”
“The real tipping point for digitally enabled data and analytics came a mere 27 years ago when for the first time, the cost of storing information on digital media dropped below that of storage on paper.”

“At the ATO, we see data as the third note of a triad between business, technology, and data.

“What exactly is XOps? Honestly, we've spent a lot of time asking ourselves the same question. If you go out there and try to find someone who can tell you what XOps is, you won't find it… although I'm sure you'll find a few consultants that will sell you an answer for a few $100,000.”

--- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week in the Data Futurology podcast, we have a special presentation to share. Ben Taylor, the Assistant Commissioner for Data Insights at the ATO was one of the leading keynote speakers at our recent OpsWorld event in Sydney. There, he provided delegates with a deep dive into the data journey for the ATO in recent years.

As you can probably guess, the ATO handles millions of lines of data every day, across data lakes that are petabytes in size. With a data team of around 800, there is often the sense that they’re racing against chaos to deliver. However, in recent years, the effort to transform and modernise the approach to data has been highly successful. The ATO was able to transition to cloud-driven data systems, and is now seen as a deep and strategic partner to the other lines of business within the organisation.

In this presentation, Taylor shares some open and transparent examples of the challenges that the ATO faced, the steps that they took to embrace AI and automated analytics while maintaining human oversight and decision-making, and how the data team went about building trust to earn the support of the other lines of business.

He also overviews the value of XOps – what that means from the ATO’s perspective – and why all data leaders should be looking at defining and adopting a XOps approach to their own data strategy.

For deep insights into one of the largest data-driven organisations in Australia, Taylor’s presentation on the ATO’s experience with data is essential.

Connect with Ben Taylor: https://www.linkedin.com/in/ben-taylor-962a2a60/?originalSubdomain=au

Joins us for our next event Advancing AI Melbourne https://www.datafuturology.com/advancing-ai-melbourne

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

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The future of AI is dynamic and multi-faceted.In this episode of the Data Futurology podcast, we are thrilled to welcome Nikita Atkins, the Artificial Intelligence Executive at NCS Australia. With NCS being one of the leading voices in AI, both in the APAC region and globally, Nikita has more than a few insights to share about the future of the technology and its most exciting use cases.

We start by talking about low code/no code and how, by embracing that and enhancing it with AI, an organisation can shift their data science team away from “run-rate” models and tasks to instead focus on the highest value items.

From there, we talk about data cleaning and pipelines, before moving on to some of the exciting innovations that are coming to the AI space – how will AI assist in rebuilding digital trust after so many high-profile cyber breaches have shaken the confidence of Australian consumers? How can AI play a role in enhancing the sustainability credentials of organisations? And what are new concepts like AI ops and Explainable AI, and how is NCS set up to be a pioneer in this space?

This is a far-reaching and in-depth interview, you’ll get a good sense of how organisations will be transforming their AI environments in the years ahead.

Don’t forget! NCS will be at the Data Futurology Advancing AI conference in Melbourne in May. Be sure to come up and speak to Nikita and his team!

Connect with Nikita: https://www.linkedin.com/in/nikitaatkins/

Learn more about NCS: https://www.ncs.co

See NCS at Advancing AI Melbourne: https://www.datafuturology.com/advancing-ai-melbourne

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

WHAT WE DISCUSSED

00:00 Introducing Nikita Atkins and the topics for the podcast.

1:04 Nikita’s background, his role at NCS, and a company overview.

4:16 On the topic of generative AI – what’s behind the interest and excitement in this area?

5:43 How generative AI tools can be effectively used in the enterprise.

9:46 On the subject of AI and low code/no code – how can organisations implement AI in a way that can enhance this area?

12:20 What should organisations be thinking about in terms of governance or deployment challenges with regard to low code/no code?

15:20 In terms of data cleansing, do we get better outcomes from better quality data and better structured model data?

18:36 Data pipelines are a critical need for any business working with data – what role does automation have to play?

23:15 The advantages of standardising data collection.

25:56 The emergence of and benefits behind AI ops.

29:36 NCS and sustainability – how can data be part of the solution?

35:17 Digital trust – in the wake of so many cyber breaches, what can enterprises do to earn the respect of customers back?

38:05 The concept of Explainable AI – what is it, and why is it a focus for NCS?

EPISODE HIGHLIGHTS

“One of the key things that we see more, particularly those organisations that are very mature in data science, is that they are still making interesting choices, where data scientists still collect the same raw data in different ways. They're still cleaning it in different ways. And then they're doing ML. What we’re looking at is whether we can actually automate that process.”
“80% of scientists will admit to you that they don't like doing data cleansing. Well, let's automate that, standardise that and let them do what they do best."
“Some of our big clients have excellent science teams. But the problem is data scientists are not the cheapest people resources around. So a lot of organisations may have 10, 15, and perhaps as many as 50 data scientists. But if you take the power of low code, and you give that to the broader business, then you're unlocking the power of numbers.”

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This week in the Data Futurology podcast, we have a special presentation to share. Ben Taylor, the Assistant Commissioner for Data Insights at the ATO was one of the leading keynote speakers at our recent OpsWorld event in Sydney. There, he provided delegates with a deep dive into the data journey for the ATO in recent years.

As you can probably guess, the ATO handles millions of lines of data every day, across data lakes that are petabytes in size. With a data team of around 800, there is often the sense that they’re racing against chaos to deliver. However, in recent years, the effort to transform and modernise the approach to data has been highly successful. The ATO was able to transition to cloud-driven data systems, and is now seen as a deep and strategic partner to the other lines of business within the organisation.

In this presentation, Taylor shares some open and transparent examples of the challenges that the ATO faced, the steps that they took to embrace AI and automated analytics while maintaining human oversight and decision-making, and how the data team went about building trust to earn the support of the other lines of business.

He also overviews the value of XOps – what that means from the ATO’s perspective – and why all data leaders should be looking at defining and adopting a XOps approach to their own data strategy.

For deep insights into one of the largest data-driven organisations in Australia, Taylor’s presentation on the ATO’s experience with data is essential.

Connect with Ben Taylor: https://www.linkedin.com/in/ben-taylor-962a2a60/?originalSubdomain=au

Joins us for our next event Advancing AI Melbourne https://www.datafuturology.com/advancing-ai-melbourne

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

WHAT WE DISCUSSED

2:17: Introduction to Ben Taylor’s presentation.

7:06: Taylor introduces himself and provides the historical context for the ATO’s approach to analytics.

12:53: Taylor describes the state of the ATO’s data environment eight years ago, and overviews the transformation project to modernise it.

14:24: Taylor describes some of the challenges that the ATO found with centralising the data and analytics function.

16:42: XOps in the ATO – what challenges led to the ATO approaching data this way, and what impact did it have?

18:03: What, exactly, does “XOps” mean to the ATO? Ben shares his insights on the conversation.

19:27: Taylor shares an example of what XOps looks like in action at the ATO.

23:45: How did the ATO avoid becoming too process heavy, as Government agencies can at times become?

27:13: How can teams handle the sense of “chaos” that comes from increasing demands from lines of business, while also managing the legacy tech debt?

29:13: Q & A with Taylor from the audience.

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This week’s guest is a true veteran of the data industry (and one of the first people we interviewed on the Data Futurology podcast!). Sandra Hogan has some of the deepest and most experienced views on how data teams can effectively engage with their organisations. Fifteen years ago, she was the Director of Customer Intelligence at Telstra, and in the years since she has seen data grow as a priority, and the status of data teams within those organisations that look to take a disruptive and leadership position in the market increase in-kind.

Sandra is now the Co-Founder and Data Analytics Lead of Amperfii, an organisation that provides analytics that helps data teams measure and articulate their value back to the organisation. In this podcast, she shares insights on how she has managed teams and encouraged their deeper participation in the organisation.

Sandra also talks about how data teams can be motivated, where they should be focusing their energies within increasingly busy organisations. She discusses how critical it is for data teams to be involved as early into the process as possible.

“You need to pick the areas where you say ‘okay, these are the big strategic things, and these are the pieces of work that I actually think really make a difference to the business,’ and focus on them,” Sandra explained in the podcast. “Even if it's only 20, or 30 percent of what you do, it's going to actually be a lot more than what you're trying to show otherwise.”

To hear more from Sandra, we are privileged to have her speaking at our Advancing AI conference in Melbourne. Click here for more information and to register to attend what will be an insightful and thought-provoking event in Melbourne, from May 3-4.

Thank you to our sponsor Talent Insights Group!

Connect with Sandra: https://www.linkedin.com/in/sandra-hogan-9409421/

Learn more about Amperfii: https://www.amperfii.com/

Join Sandra at Advancing AI Melbourne: https://www.datafuturology.com/advancing-ai-melbourne

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

WHAT WE DISCUSSED

0:00: Introduction

2:16 Reflecting on Sandra’s experience in the industry, including lessons learned in analytics and leadership.

09:51: What are the drivers and motivators that are behind the dynamics within analytics teams at the moment?

15:32: What are the main hurdles and challenges that data teams face when aiming to maximise their impact on the organisation?

25:33: How can data teams become more involved earlier in the process, and why is this important for outcomes and team motivation?

34:15: How can organisations prove and track the value of the analytics team?

40:21: Why a “conga line” is a great analogy for the role of data teams.

43:45: Why being able to capture and articulate the value of projects is so critical to data teams.

46:02: Conclusion and final thoughts.

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For our milestone 200th Data Futurology podcast, we have the immense fortune of being able to host Gina Papush, the former Global Chief Data & Analytics Officer of wellness and insurance company, Cigna.

Papush has a long history in data science, having been involved in modelling and coding from before the time where “data scientist” was a defined role. In the years since, she has observed that enterprises have become siloed across computer science, data science, and other roles, and that the next stage of data science evolution now is to now break those silos down and find ways to bring cohesion across the organisation.

She has also seen the role of the CDO and their remit evolve, from one that focused on governance and controls, to being a value creator within the organisation. Being an effective agent for change has been important to that evolution, she says on the podcast, and data executives need to look to the “blind spots” that they might have. Many have the technical skills to excel in analytics, but building skills in influence and thought leadership, and to be a partner to the other stakeholders of the organisation, is the next critical step for the CDO.

Finally, Papush also shares her insights on how value is extracted from data. A “one size fits all” approach cannot work, she says, and organisations need to build their strategies based on the maturity of their own data practice, rather than the hype in the market.

Once the maturity is there, she says, data scientists can start looking at real life-changing innovation. “It’s (data) a huge part of how we move healthcare to be more preventive and more interactive,” she said. “Health is currently very event driven. But analytics and AI could make it much more seamless and unlock real-time care.”

Tune in to the full podcast for more of Papush’s thoughts on the history and future of data science.

Thank you to you our sponsor, Talent Insights Group!

Join us for one of our upcoming events: https://www.datafuturology.com/events

Join our Slack Community: https://hubs.li/Q01gKNBn0

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This week on the Data Futurology podcast, we have a special guest, Eliud Polanco, to talk about an innovative approach to data management and access. This approach promises to make models and the management of data more reliable and secure.

Polanco, who is the president of Fluree, looks to a blockchain-driven future for data, where data blocks sit on the ledger, and the ability to access and modify them is based on a zero trust approach.

This unlocks innovation, Polanco says on the podcast, allowing organisations and individuals to take greater control over data, and do so in a more efficient manner. He points to GDPR regulations as a good example of where this approach can help. Currently, GDPR regulations require a lot of paperwork, but it’s inefficient and often ignored by both consumers and organisations. However, through a blockchain-based, decentralised approach to data management, a person’s right to control their data can be enhanced, but in such a way that the organisation can also manage the data more efficiently and effectively.

Polanco also provides an excellent potential use case in the financial services space. Financial services have strict regulatory requirements to monitor for money laundering and other illegal activities. However, that can be difficult to do based on data privacy and other regulations. In the example Polanco gives, it is difficult for a US financial services organisation to monitor transactions with Singapore, because US organisations can’t easily get access to Singaporean financial data.

However, this decentralised approach opens up the opportunity to have automation query data and return answers without the agent ever needing to see or touch the data. Suddenly it becomes possible to note a transaction without seeing the data of the transaction itself.

Enjoy this in-depth and nuanced discussion about one of the more exciting innovations on the data horizon.

Connect with Eliud Polanco: https://www.linkedin.com/in/eliud-polanco-977529131/

Contact Fluree: https://flur.ee/contact/

Read Fluree's Whitepaper on Data-Centric Architecture: https://flur.ee/wp-content/uploads/2023/01/Data-Centric-Technology-Architecture.pdf

Thank you to our podcast sponsor, Talent Insights Group!

Join us in Sydney for OpsWorld: https://www.datafuturology.com/opsworld

Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng

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This week’s guest on the Data Futurology podcast is a 30-year veteran of technology, across many different segments and roles. Nastaran Bisheban, now CTO of KFC in Canada, has previously held roles at Rakuten Kobo, Canadian Tire, and RIM. She additionally sits on the Board of Directors for the CIO Association of Canada.

One of the biggest changes that have occurred over the course of Nastaran’s career is the deeper integration of technology roles into the broader business. As she explains in the interview, Nastaran completed an MBA at Harvard Business School because her role has become an even 50/50 split between managing technology and interacting strategically with the rest of the C-suite and groups within the business. Having that broader understanding of business has been enormously valuable to her career.

Beyond that, Felipe and Nastaran discuss the role that AI and machine learning is playing in KFC Canada’s operation, how executives are encouraged to spend time “on the floor” in restaurants to learn the day-to-day challenges in operation, and how the company is looking to align its business strategy and data practice to drive growth across the operation.

Ultimately, as Nastaran stressed, technology is there to support the human element of the company. Technology that is used to help identify blind spots or notice trends that can be addressed, is an example of technology that has been deployed effectively. For instance, KFC Canada recently completed a significant transformation project to allow for all the delivery and ordering apps to integrate into its systems. It was Nastaran and her team’s human-centric approach to the application of that technology that allowed the project to succeed.

Enjoy this in-depth and insightful podcast!

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Connect with Nastaran Bisheban, Chief Technology Officer at KFC Canada: https://www.linkedin.com/in/nastaranb/

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What we discussed:

00:00 Why you need to follow the problem (opening quote)

03:17 Nastaran shares her role and remit at KFC.

04:41 What is Nastaran’s vision for a data driven enterprise in her role as a CTO?

05:47 “Following the data” – what are the projects that KFC has undertaken with this mission in mind?

09:18 How data can be collected as an asset, but become a liability.

10:41 How AI and machine learning is being leveraged to unlock innovation at KFC Canada.

13:09 Is there a point in a technologist’s career where they make the transition to focusing more on the human side of technology?

16:55 How KFC Canada is looking to align the business strategy and data practice to drive growth through the organisation.

20:06 What makes a good CTO?

23:14 What should a technologist do to learn about the business, outside of the technology?

25:37 How organisations can prepare technical people for business roles.

28:13 How an organisation of the scale of KFC Canada looks to balance out the need for innovation with the need for stability.

31:27 Clearing technical debt is important, but how can a technology team determine what areas to focus on first?

32:49 How Nastaran and her team overcomes resistance and gets buy-in to their projects.

34:44 How KFC Canada built and managed its technical team culture.

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In this latest episode, Felipe takes an in-depth look at the strategy and tactics behind AI and modelling, and looks at where organisations might be driving through the year.

One of the first things to understand is the difference between tactics and strategy. Strategy is the broad view – the understanding of where the organisation wants to be, while tactics form the pathway on how to get there. Too often organisations mix tactics and strategy up, and allow a narrow focus to dominate their approach to data, models and AI.

By looking at the big picture, 2023 will see an explosion in the number of models that are created, and the proliferation of AI and machine learning across the enterprise. Currently, the focus is on a small team of data scientists creating models of high value, but the future will see the number of models being created balloon out to thousands, driven by AI across the organisation.

There will also be an ongoing trend that more people across the organisation develop a basic understanding of models and AI, so they can deploy and monitor these models within their own teams. The question then becomes what does this mean to the data scientists? As we discuss, the role of data scientists will remain as critical as ever in creating those big-value, transformative models and managing the change management across the organisation. Indeed, as teams are increasingly able to create the smaller models for themselves, the role of the data science team as disruptive innovators is only going to become greater.

Tune into the podcast for these insights, and many more.

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What We Discussed:

00:00 Welcome to Data Futurology

02:34 The one-line summary of what a data analytics and AI strategy is.

05:19 Thoughts on the growth of data models. Currently, organisations have a small number of models in production, but the future will see organisations running with thousands of models in production.

7:51 The way that I like to apply automated machine learning solutions – as a first pass of models and a first benchmark before deploying something at scale.

9:26 The two “extreme” approaches to creating AI in organisations and kickstarting the journey towards deriving value from them.

Quotes:

Strategy defines where you are, as an organisation, looks at where you want to be, and then fills in the path in-between, which is where the tactics come in.
We’re going to go from 10, to 20, to 1000, and then 10,000 plus models in production. This is exciting – a little scary, but it’s definitely the case that we will want to have AI embedded throughout the organisation, supporting every decision in every process.
We can have a workforce that can create machine learning models, and help themselves and their teams on the daily tasks… we’re moving towards a world where more people in the organisation have a little knowledge of machine learning and AI.
We will always need that team of specialists to be working on the high value items, and to improve the models that have been created by people in the business.

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Organisations face multiple challenges when it comes to building teams in 2023. On the one hand, there is a skill shortage in just about every field of data science and analytics. Finding and attracting the best people to the organisation can be difficult.

On the other hand, there is mobility between jobs unlike anything we’ve seen before. The “Great Resignation” is still a major trend sweeping across Australia, and employees will be more than willing to move on if they don’t feel like they’re getting what they need from their jobs.

In this episode with Data Futurology podcast host Felipe Flores (a Chief Data, Analytics & Technology Officer himself), he explores both sides of this particular coin. In the first half, Felipe shares key insights and tips on how to recruit the best talent, including mistakes that he’s made in hiring and how he now looks at the interview and hiring process.

The second half of the podcast is dedicated to providing tips for retention. Contrary to the popular view, it’s not always a matter of remuneration. Indeed, studies consistently show that this is far less important to many employees than things such as the opportunity to build their skills or engage more deeply with their organisation. As Felipe says “People might want to become a product owner, or a strategic person that interfaces with the business and helps them to contextualise the results to the organisation.”

Tune into the podcast for these insights, and many more.

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What We Discussed:

00:00 Welcome to Data Futurology

0:29 What to expect from our upcoming event on operationalizing security for business value, impact and scale, at the Sofitel Wentworth in Sydney on March 14 and 15.

2:20 What makes hiring so challenging.

2:50 Three tips for hiring. Tip #1: Attitude.

3:40 Three tips for hiring. Tip #2: Transparency and openness.

5:53 Three tips for hiring. Tip #2: Be impressed with one technical thing in one technical area.

7:54 Why retention is important, and what is being done to improve it?

9:54 Three tips for retention. Tip #1: Provide formal training.

10:24 Three tips for retention. Tip #2: Give employees exposure to new work/projects.

11:31 Three tips for retention. Tip #3: Provide on-the-job training

Quotes:

Even having technical tests doesn't really show the full depth and capability of a person. It’s very easy to get it wrong.
When I was more junior in my hiring career, I would test people in the interview. We always had a technical test, and then an interview where we were going through the code, they were just wrong. This is terrible, but when I was junior, I would sometimes tell people “Hey, that’s wrong.” The idea was that if someone responded “oh, yeah, let’s discuss that” then those were the people we wanted to hire. That’s not a very effective way to do it.
I don’t look for somebody to impress me with general data engineering or data science skills. Rather, it could be something like the way they use one algorithm in a particular way.
You want one technical thing that people do well because it shows passion, commitment, and that they really care.

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This week on the Data Futurology podcast we are thrilled to welcome Tamara Mirkovic. Mirkovic is a specialist in data platforms, AI, machine learning and predictive analytics, and was recently the recipient of the Women In Digital National Award.

Mirkovic has experience building all-of-business data strategies and leading the change management program to get the organisation motivated behind the transformation.

It’s not easy, as Mirkovic said. When individuals and teams are already capable with data within their own silos, adopting a new platform can raise concerns for everything from cybersecurity to job stability. Even with the support of a visionary leadership team driving efforts from the top down, the success of a change management program relies on finding the right people to act as the champion within the peer group.

From there, it’s all about building a repeatable approach to the applications and models that will allow it to be rolled out to other teams across the organisation. “When you’ve got a critical mass of use cases, then everything going forward can be a version and iteration on what’s already been done,” Mirkovic said. “Our intention is to create enough patterns that can then be reused very quickly for other use cases.”

Finally, Mirkovic discussed what it means to have won the Woman In Digital award, and the challenges and opportunities that face women in the sector in general.

Tune in to hear these insights, and many (many) more from this wide-ranging and detailed interview!

Please note: The opinions expressed by Mirkovic in this podcast are hers alone, and not a representation of the organisations she has worked for and mentions.

Enjoy the show!

Thank you to our sponsor, Talent Insights Group!

Connect with Tamara: https://www.linkedin.com/in/tamaramirkovic/

To learn more about Women in Digital: https://www.linkedin.com/in/tamaramirkovic/

https://womenindigital.org/

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What We Discussed:

00:00 Welcome to Data Futurology

2:36 Introduction to the podcast and guest, Tamara Mirkovic.

3:09 An overview of Tamara’s experience and current role.

4:08 On the subject of building a data strategy program from the ground-up: what has been the most effective approach?

5:16 When looking at a wide-scale transformation project, how do you break it down?

7:25 What are some of the major challenges that you’ll come across when undergoing digital transformation and data transformation?

11:42 What can be done to encourage the data scientists within the organisation to change the mindset around to use common tools and approaches, to build a more collaborative environment?

17:55 How did the leadership team support the initial idea, and then the resistance that came at first?

20:22 How did you go about finding that group of champions that would help drive the change management and success of the project?

23:34 What are some of the common fears that come up during change management, and how do you address those concerns?

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The past 18 months has been a period of unprecedented innovation across data science, machine learning, and AI. The depth of research and what has been brought to market has empowered data scientists in ways that, even a year ago, could not have been predicted.

Looking forward to the next 18 months, the industry is not going to rest on its laurels, but the question is where the next waves of innovation will come from. That is what Felipe discusses on this episode of the Data Futurology podcast.

He highlights four areas in particular where he would like to see the industry focus its innovation. Starting with the ease in which to undertake data preparation, and moving through to developing better machine learning ops and engineering, the combined “key areas of innovation” would allow people working in data science and beyond, into citizen data science, to better leverage the opportunities of AI and machine learning, and at speed.

Felipe then rounds the discussion out with a look into the ethics of data science. There is a lot more discussion that needs to happen in this area, he argues, so that organisations of all sizes across the world can be sure that they’re delivering the models that have the positive impact on the world that we’re all looking for.

It’s going to be an exciting year ahead for everyone involved in data science! Tune into the podcast for more insights.

Enjoy the show!

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What We Discussed:

00:00 Introduction

1:40 An overview of the innovation that has been brought to data science over the past few years.

2:33 Four areas where the industry can innovate further #1: Making it easier to do data preparation.

4:40 Four areas where the industry can innovate further #2: Democratising AI and empowering the citizen data scientist.

6:45 Four areas where the industry can innovate further #3: Automated machine learning can still be improved.

8:33 Four areas where the industry can innovate further #4: Better machine learning ops and engineering is important to being able to reliably deploy, monitor track and alert.

12:22 Do we have the data that we need to make the responsible models that we want to?

Quotes:

Understanding what version of data was used for a particular model, and being able to create an end-to-end link is still largely an unsolved problem.
We have to move into a world where more people in the organisation need to be able to have these AI tools at their fingertips, be able to use them and be able to get them to a point where there’s value being created from them. The barriers are still typically too high.
I’m not saying that the algorithm itself needs to improve, as that’s happening with research. Rather, this is around the creation of algorithms at speed and at a scale in a way that’s more reliable and flexible, which will make it more accessible, and increase the breadth and reach of AI in organisations.

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This week on the Data Futurology podcast we speak to Sanchit Juneja, the Director of Data Science and Machine Learning at Booking.com. Having worked in roles across SE Asia and Africa before landing in his current role in the Netherlands, Juneja has a truly world view of the role of data in business, especially within the context of product development within large enterprises.

One of the challenges that large enterprises face with product and data is the question of whether you should build or buy the tools that the organisation uses. As Juneja states, the ideal approach is a holistic one that does focus on speed to market. “You do still want to build things that are strategic and core to your heart,” he said. “However, having access to things that bring you faster to market is important at the end of the day, as you want to unlock business value.”

Juneja then shares insights around the skills that it takes to work in product management. Compared with some other areas of data science, it is perhaps not quite as important to be technical (though being “tech aware” is essential). However, those in product management need to be very good at building consensus across the organisation, from executive right through to those that will implement solutions. They also need to be very comfortable with ambiguity and working with the unknown and have an appetite to learn on their feet.

Finally, Juneja shares his insights around how he and his team track the value that they’re adding to the organisation. Critical in building alignment across the business is the ability to show results, so everyone working in product needs to be able to clearly articulate the gains there.

For these insights, and many more in the wide-ranging interview with Juneja, tune into the podcast!

Enjoy the show!

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What we discussed:

00:00 Introduction

03:31 What is the main focus of your role on booking.com?

07:42 How have you found getting the stakeholders and team members on board for the journey?

09:08 How do you define the product work in in our space?

11:57 What are some of the other skills that you see as key for the product manager role?

13:28 How do you make the trade-off decisions around the product as you're implementing or building towards the vision? What are what are some of the trade-offs that need to be done in the in the product decisions?

14:30 What is the mindset shift that that you would recommend for people that may be doing ad hoc pieces of work, or a one off?

16:07 What are you most proud of?

17:14 What are some things that you would have done differently?

Quotes:

· Even if you're a big tech org, you don't necessarily need to build everything yourself. So the build versus buy call is something that is personally on top of your mind, if you're a product leader. There are so many things that are happening, so many core things that if you go on and build it inside your house, it will take you the next six months. But if you just buy a tool outside in the industry, it will be much quicker for you. I think that is one thing that is always on top of your mind, what to build versus build to buy.

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On the Data Futurology podcast this week we have AI expert and author, Cortnie Abercrombie. Abercrombie is the CEO of AI Truth, an organisation that empowers business leaders to leverage AI in an ethical and innovative manner. She is also the author of What You Don’t Know: AI’s Unseen Influence On Your Life And How To Take Back Control.

We start the conversation on the podcast talking about the challenges that data scientists face with data governance, and the many challenging questions that complicate that.

Then we discuss the challenge of maintaining models, and what that means for the safe shepherding of data. As Abercrombie notes, the average tenure of a data scientist at an organisation is only 12 to 18 months. When an organisation is managing dozens, if not hundreds or even thousands of models, it can become difficult to maintain the quality and integrity of the underlying data.

As Abercrombie notes, the stakes for this might be very high indeed. “Think about robotic-assisted surgery,” she said. “If there aren’t the proper constraints and management of the data, what’s to say you couldn’t cut a hole bigger than a person can handle, because the AI “sees” cancer material that is significantly larger than it actually is?”

Another challenge that we discuss on the podcast is the structure of teams within the organisation, and how, particularly with regards to larger companies, oversight into the applications being developed is too siloed. According to Abercrombie, with too many enterprises there’s a lack of consistency in processes and company-wide oversight and policy across those teams.

One of the key steps that is being overlooked in the rush towards AI, Abercrombie notes, is data literacy. Organisations and individuals need to redouble their efforts to truly understand data first. Because without that, the ethical application of AI is always going to be a difficult question.

For more deep insights into the thinking that is driving ethical AI and how enterprises are thinking about it, tune into the podcast!

Enjoy the show!

Find out more about Cortnie’s book at Amazon

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What we discussed:

00:00 Introduction
03:56 Cortnie outlines why AI needs regulation and draws on some of her experience as an advisor to Fortune 500 companies on responsible artificial intelligence
07:24 Felipe and Cortnie discuss the importance of having a conversation about data governance in the industry
18:55 Accountability and kill switches in Intelligent Automation
26:06 Corporate AI ethics best practices she has been working on
32:16 Felipe and Cortnie talk about the concept of an external review committee in the AI industry

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AI is a powerful tool, and as enterprise and government find more sophisticated ways to leverage the technology, there will be untold benefits returned to customers. At the same time, the responsible use of AI is of significant concern to the global population, and people are watching how its use is regulated closely.

On this week’s Data Futurology podcast, Felipe Flores presents an update on the status of regulation across Europe, China, and the US, and poses the question about whether AI regulation needs to be a global, rather than regional response.

Perhaps surprisingly, China’s taken the lead in regulating how business uses AI, Flores said. “The regulation says that businesses must notify users when an AI algorithm is playing a role in determining which information to display to them and give users the option to opt out of being targeted. The regulation also prohibits algorithms that use personal data to offer different prices to different consumers. It is really interesting that China moved early.”

Meanwhile, in the EU, the drafted regulation would categorise AI applications into one of four “risk” profiles, with oversight and accountability being scaled in kind. And in the US, much of the focus around regulation at the federal level is concerned with the potential for discrimination, while states are being left to develop their own broader frameworks.

Australia, which doesn’t yet have regulation, does have an ethical framework, which is an indication of where future regulation might go. Flores runs through that framework in this podcast as well.

For an in-depth look into the exciting and dynamic discourse around AI regulation across the world, tune into the podcast!

Enjoy the show!

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What we discussed:

00:00 Introduction

2:05 Discussion around AI regulation and how should different countries tackle it.

2:50 How have the US, China and the EU approached this.

5:00 EU regulations

8:05 USA regulations

9:40 Thoughts and comparison on the three approaches.

11:10 What’s happening in Australia.

Quotes:

· In March 2022, China passed a regulation that governs companies and their use of AI. The regulations applies to online recommender systems. They say the AI needs to be used in ways that are moral, ethical, accountable, transparent and that disseminate positive energy.

· Companies (in China) are expected to submit their algorithms to the government for review when they are being used at scale.

· The EU separates the ways AI can be used into four bands according to the risk involved. They have minimal risk, limited risk, high risk and unacceptable risk. The unacceptable risk covers things like social surveillance, facial recognition, etc.

· The US congress enacted a National AI Initiative Act, focused on improving research development, understanding AI and having an AI strategy within the country.

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Today on the Data Futurology podcast, we have Ann Sebastian, Senior Data Scientist at Wesfarmers OneDigital, as a guest on the podcast.

As Sebastian says, she is “in the trenches” building data science products. One of her key projects in recent years has been OnePass, a subscription service that provides free delivery and other services across a range of Australia’s top brands.

“It’s an incredible experience to be part of a journey, developing an idea through to proof of concept through to production ideation to a system that is adopted across the organisation,” she said. Through the podcast, Sebastian offers some key insights into that process via some of the projects that she has worked on over the years.

Sebastian also spoke about how data science teams can be built, and how a culture of innovation can be structured within them. For just one example of this that she shares on the podcast, in her current role there is a focus on learning and development, which manifests as 10 per cent of each person’s work time being dedicated to research activities.

For her part, Sebastian is currently using that research time to work on multimodal product classification, she said. “Given the fast-moving nature of retail catalogue, and need for us as a division to form a unified view across all our divisions products, there is business significance for this research project.

“We then have fortnightly quick check ins to discuss the progress on our research projects, and that really helps us to learn from each other. This is one way that data science is embedded into our day to day in a way that makes it more real for us.”

For these insights and more on how data science products are built and evaluated, and how data scientists can be motivated and innovative within their careers, tune in to the full podcast.

Enjoy the show!

Click here to learn more about OnePass

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What we discussed:

0:00 Introduction

3:00 Ann talks about her experience and her remit at Wesfarmers

7:44 What are some of the use cases you’re proudest of?

15:12 Ann shares more information about her favourite use case and how it evolved from an idea to the start of the technical work.

17:58 How did you measure business impact?

20:14 How did you operationalize the models?

24:00 Can you describe your current role?

31:23 What’s your advice for people wanting to get into data science?

Quotes:

· Business teams can sometimes view data science as a mythical creature, so I love working with them to demystify data science and achieve business benefits through it.

· The use case that I'm proudest of is the automation of the complaint classification, where we implemented various natural language processing models to predict the category of the complaints using real time models.

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This week on the Data Futurology podcast, we have three special guests to share insights on how data works in retail settings. Nick Merry, the Head of Analytics at flybuys (Loyalty Pacific), Kathryn Gulifa, the Head of Data and Analytics at Catch, and Stuart Garland, the Director at Talent Insights Group, join us for a wide

ranging and in-depth look into how analytics are changing and the impact this is having on teams.

“The really good analysts that I see are the ones that are able to crystalise their understanding of what a business is trying to solve, and solve for that problem in

particular,” Gulifa said. “I always think that the technical skills can be taught if you’ve got the aptitude. With the technology landscape changing so rapidly, if

you try and peg yourself to recruiting people that have experienced only particular tech, you're really limiting your options.”

As Garland then notes, those that focus purely on their technical capabilities would limit their career development opportunities, unless they’re willing to learn how to engage with the broader business anyway: “Even if you’re not leading people, you still should be learning the ability to demonstrate the value and impact that a project is going to have on the business at a more senior level,” he

said.

As Merry also notes, the days where the data team would be separate from the other lines of business are largely over. Now, the digital team is integrated into everything from marketing to security and governance, and people on that team need to be able to have conversations across all of them. “Having digital analytics, not as separate functions, but more integrated with the broader view, is one of the encouraging things that I’m seeing,” he said. For more deep insights from these three thought leaders on the changing dynamics of work in data and analytics, tune in to the podcast!

Enjoy the show!

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Quotes:

I’m not a fan of the data translator role because I feel it absolves data analysts from developing the skills of consultation and defining a problem. What differentiates good analysts from really good analysts, is understanding the business context and the ability to drill down into what's actually important to the business.

When it comes to recruitment, I always think the technical skills can be taught if you've got the technical aptitude. The technology landscape is changing so rapidly, all the time, that if you really try and peg yourself to recruiting people that have experienced only with particular tech, then you're really limiting your options. I think what you should be trying to find people that have not necessarily the polished and ready to go consulting skills, but the curiosity, the engagement, the wanting to understand why they do something, and what impact their work actually has on the business that they work for.

Considering people with longer or shorter tenures depends on what the role is and what you want from that individual. If you're in the process of building a platform and bringing in a data engineer that has gone across three or four different builds over the last four or five years might be useful because from that perspective, you've got three or four different pain sets, lots of experience in regards to what went wrong and, more importantly, what went right.

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This week on Data Futurology we answer a burning question that is asked in the data space a lot: just what makes a good data strategy?

As we discuss, there is no one-size-fits-all approach to data strategy that will work for all organisations. This cannot be approached like a templated “best practice” to business. Instead, there are three factors to consider when devising the data strategy that will work for your business:

1) Defining where the organisation is on its journey today. How a business just starting out with data needs to approach strategy is different to an organisation with a mature data practice.

2) Deciding on where the organisation wants to get to. This refers to the need for organisations to get buy-in across the business to a data strategy. Without that alignment the project is prone to failure.

3) Developing the execution path, to take the organisation from where it is now, to where it is going to be. The better defined this pathway is the more likely it is that the project will stay on-track and on-goal.

What distinguishes a good data strategy is one that is aspirational in nature. “Aspirational” doesn’t mean that the goal needs to be futuristic or difficult to achieve. It could be grounded and realistic, and simply an effort to step up from where the organisation is currently. But having that clear goal and a vision for the value it will deliver to the organisation at the end of the journey is critical to motivate the effort behind the data strategy.

In this in-depth discussion, we lay out the approaches and use cases that motivate successful data strategies and highlight how organisations can approach each stage of the journey.

Enjoy the show!

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WHAT WE DISCUSSED 0:00 Introduction

1:09 How do you devise a data strategy? What sets apart the good from the bad in a data-driven strategy?

3:30 Data strategies encompass everything, they are broader than analytics, AI and tech strategies.

5:10 Get alignment on where the organisation is today and where it wants to be in the future.

7:45 The importance of having a prioritised set of use cases for your data strategy.

10:05 The four components needed to help you prioritise data use cases.

12:50 The two sides of organisational readiness.

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This week on the Data Futurology podcast, we have the special privilege to host Dr. David A. Bader, a Distinguished Professor at the New Jersey Institute of Technology, and the inaugural director of the Institute for Data Science there.

Bader joins us on the podcast to discuss massive graph analytics, a topic that he is a recognised expert in and has recently published a book on. He and his team are currently working on a project that will allow anyone, via the Jupyter Notebook and Python, to leverage their data science framework, running on “tens of terabytes” of data. “It is quite exciting to democratise data science – and especially graph analytics – so that anyone with a problem that knows Python can work with some of the largest data sets,” he said.

According to Bader, graphs are now a mainstream part of data science and a way to solve the most challenging and complex problems in the enterprise. “A graph abstracts relationships between objects, and any problem that we can abstract where we have relationships between objects, we could use graph analytics to solve,” he said.

Much of Bader’s work – including through his book – is focused on helping organisations grapple with the exponential growth in data, and the impact that this has on their ability to dedicate adequate resources to work at scale. As he said, being able to do that is going to be fundamental to humanity’s ability to respond to the many real challenges that it faces ahead.

“I want equitable access for everyone to be able to work on these problems, and to find new discoveries that are important, and help solve global grand challenges,” he said. “I think that we have many issues in the world today. And if we give more capabilities to those with data, and let them empower the data will make the world a much better place.”

For more deep insights on the importance and value of massive graph analytics, tune in to our conversation with Dr. David A. Bader.

Enjoy the show!

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The motor industry has always been right at the forefront of innovation, and this is also true when it comes to embracing machine learning and AI. This week’s guest on the Data Futurology podcast is Leonard Aukea, the Head of Machine Learning, Engineering & Operations at Volvo, who shares with us insights into what the global vehicle giant is doing to bring value to the operations chain across the company.

For Aukea, it has been a story of establishing best processes across the organisation. He said that one of his first priorities was to bring the various data science teams together to minimise the impact of siloing, and encourage the machine learning practitioners to adopt software engineering principles. This might not be immediately comfortable to them, but as Aukea said, ML experts are smart people working on complex problems, and facilitating an open-minded approach across the organisation is key to driving long-term success.

“You need to start simple,” he said. “Think about processes, ways of working, and the cultural aspects, and try to fit tooling and infrastructure along that kind of endeavour. You don’t need to choose the most extreme state-of-the-art tools.”

At one point, Aukea noted, things being pushed into production were becoming unmanageable, so he and his teams took a step back and reset. “We went back and decided to focus on first principles,” he said. “We evangelised these first principles to develop good ways of working, and then adopted the infrastructure and tooling towards building AI on top of that.”

Ultimately, Aukea said, quality comes from the processes, rather than the technology. There are, of course, technical challenges, but for anyone aiming to get true value out of machine learning, the focus needs to be on the processes.

Aukea then explains how, with those processes in place, he and his team have been able to start delivering deep and valuable insights. For more on how Aukea was able to structure Volvo for success with machine learning in operations, tune in to the podcast!

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In today’s episode, we have Romina Sharifpour, Machine Learning Specialist at Amazon Web Services (AWS).

Operationalising machine learning models, particularly scaling MLOps capability across teams within an organisation is a difficult feat.

Join Romina to find out how you can easily accelerate your MLOps journey using Amazon SageMaker Pipelines. You'll gain insights into how AWS customer Carsales keeps up with increased demand in building and productionising AI models, and their strategy to democratise AI across the whole development teams. This allows any developer to be a citizen data scientist and ML engineer by leveraging Amazon SageMaker.

Enjoy the show!

If you want to learn more about building modern applications on AWS and attend a virtual conference, just google “AWS Innovate” or click the link below.

https://aws.amazon.com/events/aws-innovate/apj/modern-apps/

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This week on the Data Futurology podcast, we have the special privilege of talking to the top analytics leader in Australia, according to IAPA (the Institute of Analytics Professionals of Australia).

Brad Petry, the Executive Director – Operations, Insights, and Digital Channels at the Department of Jobs, Precincts and Regions in the Victorian Government, was awarded this accolade for his work in leveraging AI and machine learning to overcome biases in the recruitment process. He spends time on the podcast this week talking about what that means for the department, and the implications it has for recruitment more broadly.

Over the past 18 months, Petry has been driving a digital transformation program across the department, something that has been made even more challenging because it has happened through the pandemic and because the data that he and his team handle is needed on a daily basis. There was no room for downtime or mistakes while the transformation was executed.

At the same time, there was an enormous opportunity within the department to leverage automation and AI with data – in many cases for the first time – to improve the reliability of the data and productivity across the department.

As Petry says, the key to success is to remember that it’s the data that’s the important element, rather than the software or context that the data is held and analysed within. “When we started, we said to ourselves that the thing we knew, and what was going to persist, was the data,” he said. “The technology and programs will come and go, but the data is something that will always be there and everything comes back to the data.”

For a deep dive into driving a transformation agenda with data, tune in to this week’s podcast!

Enjoy the show!

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Connect with Brad https://www.linkedin.com/in/brad-petry/

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This week on the Data Futurology podcast, we talk transformation and the importance of having data engineers to guide the strategy and agenda. To provide expert insights into this topic, we have the pleasure of hosting Richard Glew, Chief Technology Officer, and Natalia Dronova, Senior Data Analyst from Aginic.

Aginic is a consultancy that assists organisations with their transformation goals, providing expertise in analytics, agile, and the digital experience.

Transformation remains a challenging goal, with research showing that most projects fail. Glew and Dronova discuss some of the reasons for this, which are many and varied, but according to Dronova, one of the big ones is that organisations make mistakes in their haste to transform quickly.

“One of the challenges with transformation are the people that want everything done within six or eight months,” she said. “They want it now, and they’re finding shortcuts to try and make it happen that are hurting them in the long run. Then, a few years later, when you look at their stack, it’s all over the place.”

Dronova and Glew then go in-depth in discussing the structural problems that can affect transformation efforts, as well as the cultural problems across organisations – the impact that a focus on data governance can have on projects, for example, and why organisations need to move to a position of data enablement.

Finally, the two also discuss the role of the data engineer. As Glew said, traditionally the role has lagged behind that of the software engineer, but with more focus being placed on their role in transformation, the rapidity with which the role is evolving, and the relative scarcity of engineers resulting in higher salaries, now is a great time to consider a career in data engineering. “With the state of data engineering today, it’s the best time to get into it, because it’s still evolving and innovating really quickly.” Glew said.

Tune in to this deep and insightful discussion to learn more about the dynamics behind transformation and the role of the data engineer.

Enjoy the show!

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Connect with Richard https://www.linkedin.com/in/rlglew/

Connect with Natalia https://www.linkedin.com/in/nataliadronova/

Learn more about Aginic https://aginic.com/

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It’s safe to say that most people in data science want to do the right thing. However, AI ethics cannot just be an afterthought done in the service of regulatory obligations. It needs to be baked into the way the organisation looks at data, at every level.

How organisations can achieve that is the focus of our latest podcast, with Natalie Rouse, General Manager of Eliiza and Brendan Nicholls, the Practice Lead, Machine Learning Engineering, joining us to discuss the topic. Eliiza is a data consultancy, and Rouse and Nicholls are right in the trenches with their customers.

There are many questions that organisations should be asking of their data, particularly with regards to how to ensure that it’s free of bias and that it’s being used accurately. As Nicholls and Rouse discuss on the podcast, the questions range from how the data’s being collected, where it came from, whether it accurately reflects demographics, and what the range of uses of the data is, based on the collection policy.

These are all relatively straightforward things to think about, but nonetheless, they’re often overlooked, especially within teams that are highly technically orientated. As the Eliiza team acknowledge, one of the challenges in data science is that teams are technical and want to “reduce” everything to numbers that can be measured. However, to fully embrace ethical AI, it becomes important to embrace the ambiguities and the non-measurable side of the discussion as well.

Eliiza is deeply engaged in helping its customers achieve this understanding of ethical AI, and regularly hosts monthly MLOps meetups in Melbourne through the MLOps Community, which hosts meetups around the world - 22 different locations - to facilitate knowledge exchange. It will also be holding a hackathon around healthcare shortly to encourage ethical AI in that area.

Finally, Nicholls will be presenting at the Scaling AI with MLOps event in Sydney on October 25 on why Ethical AI matters. Tune into this podcast and drop into his presentation to gain a deep understanding on why ethical AI is not just an obligation but, when done right, an opportunity.

Enjoy the show!

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Links

Eliiza https://eliiza.com.au/

Hackathon: https://www.intellihq.com.au/medical-datathon/

AI Australia Podcast: https://eliiza.com.au/learn/ai-australia-podcast/

MLOPs community: https://www.meetup.com/en-AU/melbourne-mlops-community1/

Connect with Natalie: https://www.linkedin.com/in/natalie-rouse-7115b15a/

Connect with Brendan: https://www.linkedin.com/in/nichollsbrendan/

Read the full podcast episode summary here.

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Healthcare is an industry that stands to benefit a great deal from data and analytics. At the same time, the sensitivity of the data in the sector is extreme and how organisations manage that data is critical.

Yalchin Oytam, the Head Of Clinical Insights And Analytics at South Eastern Sydney Local Health District (SESLHD) is right in the thick of the discussion. He joins us on the Data Futurology podcast to talk through both the challenge and opportunity.

One of the big challenges that the Australian health system faces, Oytam said, was that primary healthcare was handled by the federal government and secondary care was handled by the states. How the sharing of data between these two is handled is critical to maintaining the customer experience with their health care. More importantly, if data can be leveraged to improve outcomes in primary care, it can reduce the burden on secondary care. Oytam gives the example of diabetic patients being diagnosed and accurately cared for by their GPs have a much lower risk of an unplanned hospital visit.

“When you keep people out of hospital, it also means that they are generally healthier, more productive, and happier. In human terms, the benefit of this goes beyond money,” he said.

The other big opportunity in healthcare is the use of data modelling to personalise healthcare services. Modelling can be used to detect warning signs and risk factors, and more proactively communicate with patients. In the longer term this can result in earlier diagnosis and better risk management – and it’s just one area where this approach to data can lead to meaningful change. “The question is how do we best manage our climate, while also maximizing the quality of life for human beings, and other life forms,” Oytam said.

“A better world certainly is possible.”

Tune in to the podcast for an in-depth discussion on how data can deliver better health and lifestyle outcomes for us all.

Enjoy the show!

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Kevin Ross has had more than 20 years of experience in using data, science and analytics to lead decision-making. Now, as the CEO at Precision Driven Health, and Advisory Board Chair at the NAOI (Natural, Artificial and Organisational Intelligence) Institute, he is placed right at the heart of the data discussion in New Zealand.

He joins us on the Data Futurology podcast this week to discuss the evolving role of data in healthcare, and how it has broadened to really start to embrace personalisation. “We have this fantastic opportunity other there where we know that health doesn’t make use of all the data that’s out there,” he said. “Imagine what you could achieve if you added the computational power of AI into diagnosis and healthcare. The potential is amazing for guiding people to understand themselves and their outcomes.

“It is being driven by consumers looking for health to provide them the same services that they can get elsewhere.”

Ross acknowledges that change is coming slowly, but as it is being driven by customer demand, the change is inevitable. Those healthcare organisations that can adjust will prove to be the disruptive forces in the years ahead.

Elsewhere in this wide-ranging podcast, Ross also discusses the advances in data capture technique, and the implication that has for better analytics and AI. He also talks through the privacy and ethical implications of data use in healthcare, and how data outcomes can be understood and measured within healthcare.

Healthcare is one of the most fascinating sectors when it comes to data, analytics, and patient outcomes, and we’re only scratching the surface of it. Tune in to this in-depth conversation with Ross to get a sense for what’s coming next.

Enjoy the show!

To learn more about Precision Driven Health: https://precisiondrivenhealth.com/

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NVIDIA is best known for its production of GPUs and APIs that enable high-performance computing, supercomputing, and power some of the most intense applications across the world. Unsurprisingly, the company is deeply involved in AI, and on this week’s podcast, the company’s VP of Solutions Architecture and Engineering, Marc Hamilton, joins us to share the company’s unique insights into the field.

Hamilton explains how NVIDIA’s innovative AI Factory concept allows it to introduce efficiencies into the data gathering process. He uses the example of self-driving cars to just how effective the NVIDA approach is. To manually collect all the data on all the roads in the world, the researchers would need to travel 11 billion miles. However, NVIDIA can leverage simulations of roads to “teach” the AI powering these cars synthetically.

Hamilton then describes the fascinating advancements of digital twins – a technology idea that has been around for decades but only just now supported by powerful enough technology to handle the AI and other processing requirements for it. This, Hamilton says, can be used for everything from workplace layout simulations that “test” an environment to make sure it’s safe to work in before real humans do so, through to creating a digital “twin” of the earth as a way of testing the impact of climate change “700 or 7,000” days down the track.

“It's going to be many years before we're done, but we're already making some interesting project progress and seeing some interesting early signs of future success,” he said.

With AI certain to be critical to how humanity grapples with the increasingly complex challenges facing it into the future, it is companies like NVIDIA that will at the forefront of our response. Tune in to learn more about the very cutting edge of AI.

Enjoy the show!

About NVIDIA

Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics and ignited the era of modern AI. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry. More information at https://nvidianews.nvidia.com/

About GTC (GPU Technology Conference)

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Learn from experts how AI and the evolution of the 3D Internet are profoundly impacting industries—and society as a whole.

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This week we are thrilled to welcome Harjot Singh, who had been the CDO of RAC in WA until very recently. Singh is a true expert of and champion for transformation in the workplace, and has deep insights on data strategy and data governance to share.

“Data drives a digital transformation in any organisation.” Singh said. “Everyone wants to jump on the bandwagon around digital transformation, but most organisations struggle to understand where to start.

As Singh explains on the podcast, there is a specific order with which organisations need to approach transformation. It starts with data, which organisations should already be investing in because monetising data is one of the biggest opportunities in business.

From there the data can be leveraged into machine learning and, eventually, AI.

Related to this, however, Singh also mentions that organisations need to better understand the business drivers behind what they are doing with data and the digital transformation journey. “I wrote an article on LinkedIn that was around the five common mistakes to make in digital transformation,” Singh said. “If people think that digital transformation is only about technology transformation, it’s going to fail.”

From there, Singh and host, Felipe Flores, discuss the impact of regulation in Australia on innovation, how companies are working within those challenges, and how various highly regulated sectors – including insurance and financial services – are finding new opportunities.

Ultimately, however, as Singh says, it all comes back to data. “I say data and digital in the same sense, because I treat them as two sides of the same coin – one is incomplete without the other. Data is the bullet and digital is the gun to launch the bullet – without both you’re not going to have much of an effect.”

For deep insights on the strategy and opportunity behind digital transformation, and the deep role of data in it, check out the full podcast!

Enjoy the show!

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GEEQ (Geeks With EQ) is a non-for-profit with an important mission: helping women get into IT and boosting the diversity of IT companies in Australia. To this day less than 20 per cent of Australia’s IT workforce are women, and this has far-reading implications, from the bias that gets built into technology itself through to the depth of innovative thinking available in the space.

The two founders of GEEQ, Azadeh Khojandi and Katrin Schmidt, join us on this special podcast to discuss the work that they’re doing, and the traction that diversity is getting across Australian corporate spheres.

“It’s important for us that we’re not only bringing women into the workforce, but helping them to grow and get the promotions, more responsibilities, and the fulfilment they deserve,” Khojandi says in the podcast. GEEQ is more than an advocacy group. It focuses heavily on skills and mentoring, providing women with books on leadership and managing events to assist with knowledge transfer.

On the side of advocacy, the two are focused on helping the Australian business community recognise biases in the hiring process and how to mitigate against that, Katrin says on the podcast. “It’s really difficult to have awareness of your own unconscious bias,” she says. “It’s like stopping and thinking to yourself, ‘what did I just do?’. The first step is a change in awareness. You don’t have to jump to conclusions, but you do need to watch and be aware.”

Azadeh and Katrin are sponsors at the upcoming Data Engineering Summit and will be hosting a luncheon. It will be a rare opportunity to talk directly to some of the speakers from the summit and discuss how to tackle the ongoing challenge of diversity.

In the meantime, tune into this in-depth podcast, and hear from the experts about why diversity is a $60 billion opportunity for Australian businesses.

Learn more about GEEQ: https://www.linkedin.com/company/geeq-australia/

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Later this month, Nathan Steiner, the Director of Field Engineering, ANZ, at Databricks, will give a presentation at the Data Engineering Summit. There he will talk about the “habits” of data-driven organisations, and the importance of an open architecture that combines the best elements of data lakes and data warehouses.

Steiner kindly appeared on this episode of the Data Futurology podcast to talk about this, and further discuss the Databricks vision for data-driven workspaces.

“Historically, you look at data engineers, data analysts, AI, machine learning and data scientists, they were focused on different types of data, so you had your data engineers focused on your siloed and disparate ADW enterprise data warehousing, relational database structured systems, and you had your data scientists looking at predominantly real time data,” he says during the wide-ranging conversation.

The solution, to Steiner’s and Databricks’ vision, is bringing those data resources together and making for a more collaborative data environment. “It’s more pragmatic and effective for these job roles to be working from a single uniform platform,” he says.

As Steiner notes during the conversation, the personalisation that is so important to modern business is driven from being able to make the data resources collaborative. He highlights the example of a financial services company that wants to be able to issue credit within five minutes from an application via a smartphone. “In the back end, it's AI, and ML that is doing the credit risk assessment frameworks of that particular individual and creating that value customer experience,” he says.

Finally, Steiner considers the governance implications of the Databricks lakehouse, and the advantages of having a uniform and unified approach when it comes to governance.

For more insights on breaking down data silos and unifying data teams, be sure to tune in to the podcast!

Enjoy the show!

Learn more about Databricks

Learn more about Nathan Steiner

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Graph databases are powerful tools in analytics, but they are an often-misunderstood innovation. As they hold the relationships between data as a priority, they are an invaluable tool for modern, heavily inter-connected datasets.

In this episode of Data Futurology, we explore graph databases with Peter Kokinakos (pk), the COO of MIP. They have been conceptualised for around 18 years, but it is only now that the computing power has started to catch up to allow graph database projects to come to fruition.

MIP is right at the front of delivering these capabilities to their customers. “It’s becoming a real product,” Kokinakos says in the podcast. “All of a sudden we’ve got the capability of delivering these really intricate kinds of analytics for complex relationships.”

Kokinakos, who will be speaking at theAdvancing AI Sydney summit in August, further outlines the additional value that data scientists can get out of data relationship value in comparison to the data value. Delivering this value requires some change management to take advantage of because, as he says, “instead of just double clicking on something and drilling down the level, you can now actually drill down by the relationship.” However, once that change management process has been completed, the ability to be able to interact with customers on the basis of interconnected relationships rather than single data points is compelling.

Change management is a challenge for many organisations and data scientists – anything new is always going to have some resistance. This is why MIPS runsThe Data School, and Kokinakos explains in detail the value that adds to customers in the podcast as well.

Tune in for an in-depth discussion into the very bleeding edge of data innovation with a company at the forefront of it.

Enjoy the show!

General info about theData School

Application process and deadline for the next 3 intakes:https://www.thedataschool.com.au/apply/

Learn more aboutMIP

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For our milestone 200th Data Futurology podcast, we have the immense fortune of being able to host Gina Papush, the Global Chief Data & Analytics Officer of wellness and insurance company, Cigna.

Papush has a long history in data science, having been involved in modelling and coding from before the time where “data scientist” was a defined role. In the years since, she has observed that enterprises have become siloed across computer science, data science, and other roles, and that the next stage of data science evolution now is to now break those silos down and find ways to bring cohesion across the organisation.

She has also seen the role of the CDO and their remit evolve, from one that focused on governance and controls, to being a value creator within the organisation. Being an effective agent for change has been important to that evolution, she says on the podcast, and data executives need to look to the “blind spots” that they might have. Many have the technical skills to excel in analytics, but building skills in influence and thought leadership, and being a partner to the other stakeholders of the organisation, is the next critical step for the CDO.

Finally, Papush also shares her insights on how value is extracted from data. A “one size fits all” approach cannot work, she says, and organisations need to build their strategies based on the maturity of their own data practice, rather than the hype in the market.

Once the maturity is there, she says, data scientists can start looking at real life-changing innovations. “It’s (data) a huge part of how we move healthcare to be more preventive and more interactive,” she said. “Health is currently very event-driven. But analytics and AI could make it much more seamless and unlock real-time care.”

Tune in to the full podcast for more of Papush’s thoughts on the history and future of data science.

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For the most innovative and forward-thinking organisations, the next frontier forward for data is focused on machine learning, and specifically the role that MLOps plays in driving outcomes.

Questions that data leaders need to be asking themselves now include: What steps do organisations need to take to deliver ML maturity, how can they take the leap from experimentation to production, and how ML teams be effectively organised and motivated around this goal?

To dive deeply into this critical discussion, Data Futurology recently brought together a panel of some of the leaders in ML strategy and execution. Each of these companies have been successful in productionalising ML across their enterprises, and discuss their strategies and successes in an open and free-flowing discussion:

Agustinus Nalwan, Head of AI and Machine Learning, carsales.com.au
Farhan Baluch, Principal Data Scientist, Apple (USA)
Kendra Vant, Executive GM Data, ML & AI, Xero
Ram Radhakrishnan, General Manager Customer Analytics, AI & Data Science at Woolworths Group

These four experts also highlight just how important it is to motivate teams around an ongoing process of learning and discuss how to deliver a dynamic understanding of the changing role of data across the organisation. Whether the data team is inwardly-looking, or focused on customer outcomes, emerging concepts such as “software 2.0” – as mentioned in the webinar – will continue to throw curveballs that MLOps teams will need to have the agility to adapt to and capitalise on.

Ahead of the Scaling AI with MLOps event to be held in Melbourne on October 25, this webinar is a unique opportunity to gain insight from those at the very bleeding edge of data innovation.

Enjoy the show!

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Online wagering is one of the most sophisticated and complex fields for data and analytics. This week on the Data Futurology podcast, Mia O’Dell, the GM of Data Science at Sportsbet, kicks thing off by explaining how the company brings together three separate data teams, across three lines of business, to achieve meaningful and collaborative data outcomes.

Sportsbet is also growing its data practice and looking to nearly double its team sizes by the end of the year. O’Dell – who was also responsible for scaling the data practice in a previous organisation – also shares some insights about how to approach data scaling. There’s no “one size fits all” approach, she says. Success depends on being able to work with the teams to come up with a strong and compelling vision.

Finally, O’Dell also shares her concept of “machine learning offense” and “machine learning defence” as a way to help articulate the value of ML Ops at a time where non-data executives within enterprises are still struggling to understand the breakdown and operation of ML Ops teams.

It’s also important to understand where and when ML Ops becomes important to a business, O’Dell adds, saying that a lot of organisations make the mistake of going all-out when they’re just at the start of the journey, where the value of ML Ops will be marginal and difficult to articulate.

“If your first machine learning model is something that’s extremely critical to the success of the business, of course you want to over invest in its reliance,” she says. “But for something that isn’t necessarily core to the business, ML Ops can result in putting far too much effort on the defensive side, and not enough yet on the offensive side.”

Tune in for in-depth insights into this, and more, with Mia O’Dell.

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On today’s podcast we have special guest, Melanie Johnston-Hollitt, the Director of the Curtin Institution For Computation, to discuss with us some of the bleeding edge ways that data is being leveraged in the academic space.

For example, a new radio telescope being built in Australia and South Africa will give us new insights into the cosmos. It will also generate 160 terabytes of data per second; an eye-watering amount of data that poses unique challenges about how it’s utilised and managed. As Johnston-Hollitt mentions, where most wisdom says to store all the data collected, in this case, the research teams are better off developing ways to process the data as quickly as possible, and then removing it to make a fresh set of observations.

This understanding of how to best manage and interpret data highlights the ongoing role that data specialists play at a time where automation is taking ever-more amounts of mundane work off the hands of people, Johnston-Hollitt adds. Data automation will achieve three things in workplaces, she says:

1) It will take the drudgery away from roles, allowing professionals to focus on higher-level and more engaging & rewarding work.

2) It will supplement and complement, but not erase, the expertise of humans. Johnston-Hollitt points to how data can be used to support medical diagnosis for less common conditions that a doctor might not see frequently, but ultimately, it’s up to the doctor to make the diagnosis.

3) Data and AI will also result in the creation of new jobs, as people develop more sophisticated algorithms and need people to validate the applications and results.

Hear more detail about all these insights, and more, by tuning in now. Thank you very much to Johnston-Hollitt for guesting on this podcast.

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“Data engineers are like plumbers,” Edward Chenard, the Senior Director of Data Science and Analytics at Shipwell, said in this latest episode of the Data Futurology Podcast.

By that, Chenard means that data scientists are excellent at analysing what’s coming through the proverbial pipes, but if the “pipes” break for even a couple of hours the costs can be in the millions of dollars, and so you want specialised data engineers operating in the background, maintaining the stack and being the unsung heroes of the data practice.

This is just one insight on building effective data teams that Chenard shares on the podcast. In this in-depth discussion, he also:

Highlights the changing dynamics within teams post COVID-19 and how interactions have changed with working from home.
Discusses the disconnect between leadership and data scientists/engineers, and how many data scientists come to realise that their interests lie outside of leadership.
Talks about finding ways to develop confidence into younger data professionals.
The increasing value of humanities expertise in data.

Finally, Chenard shares his three key personality qualities that allow someone to succeed in data-based roles:

1) Creativity. If you don’t look at the space as a creative endeavour, you’re not going to be happy.

2) Curiosity. If you’re not naturally going out there and looking up stuff, going down different rabbit holes you’re not in the right mindset.

3) Openness. You need to be open to other perspectives – including those you don’t agree with – to be able to see the opportunities that come from data.

Tune in for these insights, and many more, around the dynamic roles and exciting opportunities facing data scientists ahead.

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From skills shortages to remote working, keeping data professionals happy and comfortable in their roles is a rapidly evolving challenge. This week on the podcast, Talent Insights Group Director, Ben Le Gassick, and Associate Director, Patrick Choy, are our special guests as we delve into the problems organisations face in attracting and retaining the best people.

The talent shortage means that many employees feel overworked and overwhelmed. Meanwhile, while remote work has become standard and expected, making sure that people continue to feel engaged and connected within their teams and organisation is important to avoid attrition within teams.

On the podcast, Patrick and Ben discuss solutions to these pressing problems. It could be as simple as understanding the strengths and interests of each individual data scientist, and ensuring that the work meets their personal and professional goals. Another best practice is to leverage contractors intelligently to supplement the permanent staff. It’s also important to consider how to keep the data scientist engaged once the application that they were working on has been deployed and the workload shifts to maintenance. Many data scientists leave a role after a year simply because maintenance work isn’t as engaging, so what is the solution there?

And, finally, Patrick and Ben also share their thoughts about what employees like to see in a leader. People are attracted to great leaders and even willing to follow them from one company to the next. So, what can an organisation do to make sure that they are the ones with the great leadership team?

Tune in for these insights, and many more, about the opportunities and challenges in recruitment for data roles in 2022 and beyond.

We will be covering this topic in more detail at Advancing AI Sydney with our awesome community. To join us there in person register with the earlybird discount at: https://www.datafuturology.com/advancing-ai-sydney

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This special episode is a recording from a live webinar we ran back in February as part of our Future proofing your data platforms online event covering how to establish a best in class multi-cloud strategy.

Felipe explored all aspects of a multi-cloud strategy, including simplifying your data architecture, regardless of whether your systems are running on-prem, in the cloud, or a combination of both and optimising your agility and efficiency across your cloud infrastructure.

Stijn explains that there are three main drivers that organisations should consider in a multi-cloud strategy: cost, optimisation and capability. Each cloud provider to a certain extent has the same capability, but they have their niches in certain products that they offer.

Kieran tells us that when you're on a multi-cloud journey, you need to go into it with your eyes open. People underestimate the process of how much it can cost and how long it can take in a big organisation.

So, what does it take to jump into a world of multi-cloud and is it right for your organisation? Tune in to this podcast to hear from Kieran and Stan on the best approach for a Multi-Cloud Strategy.

Enjoy the show!

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Australia’s CSIRO has always been a hotbed of innovation and technology creativity, which is why it is a special privilege to speak to Stela Solar, the Director of the CSIRO’s National Artificial Intelligence Centre on this week’s podcast.

Solar, who spent over a decade working in global roles at Microsoft in the US prior to joining CSIRO in January, said that one of her key observations on coming to Australia is that we are enthusiastic adopters of technology – we’re a leading nation on cloud adoption – we currently run a little behind on AI adoption.

There are three areas where Australia can invest to start to narrow the game and realise the opportunity of AI adoption, Solar said: Addressing the skills shortage, broadening the investment scene beyond the US, and the development of “tech cities” where the population of the area is galvanised around technology.

Another big opportunity for AI in Australia, Solar added, was for the small and medium-sized business. Just 2% of Australian businesses are currently leveraging AI solutions. This is a low base, but it also means an enormous opportunity for businesses looking forward. In the podcast, Solar shares what that might look like.

Tune in for these insights and more from one of the foremost data thought leaders working in Australia.

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On this episode of the podcast, we have the privilege to speak to Michelle-Joy Low, Ph.D, the Head of Data & AI at Reece Group. Low talks us through how data is the foundation of the ongoing transformation of one of Australia’s most venerable retail brands, having celebrated 100 years in 2020.

Low explains the importance of diversity in the workforce, and how it leads to better outcomes for the business and better outcomes for the customer. The tech space has been particularly good at recognising the importance of diversity, Low said, and Australia is a great place to work in that regard, but at the same time it’s now important to look beyond participating in the movements, and genuinely build diverse teams that are empowered to speak out about and drive further change.

Low also shares some of the challenges that come from AI, and how she and her team are grappling with them. For example, data is complex to implement and expensive… and the decision makers behind the data projects are distant from the team that builds the applications. The Chief Customer Officer, for example, doesn’t build apps themselves. So how do you tackle that challenge within the data team and deliver outcomes for the organisation?

Tune in for this deep-dive and fascinating conversation about how a business is leveraging data to drive toward better outcomes for all.

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In this second podcast with the visionary data scientist, entrepreneur and business leader, Sarah Dods, we dive into the details around product development and the role of data science.

Across her career, from NICTA and CSIRO, through to AGL, Telstra Health, RMIT University and Gerson Lehrman, Sarah has had a long history in bringing innovation to market.

One thing she says is that business leaders should not lose sight of how data science needs to work. It’s a team sport, in the same way that building a car requires more than someone to design and build the engine.

“So, what do you need to make a solution supportable, sustainable and safe?” Sarah asks, before going on to note that another challenge that data science teams need to be cognizant of is that data science models fail silently. The application could be bringing garbage in, and pushing garbage out, and it will happily keep working, producing outputs that will no longer mean what you want them to. So teams need to think about your feedback loop.

This is where agile comes from, in encouraging an iterative process in which the MVP is produced as quickly as possible, and then iterated on indefinitely as opportunities for ongoing development arise.

Stay tuned for some deep insights from Sarah about the opportunities for data science to drive next-level product innovation.

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On this episode of the podcast, we are excited to speak to Sarah Dods, one of Australia’s top tech entrepreneurs, innovators, and company leaders.

In a career that has spanned from public organisations like NICTA and CSIRO, to private companies as wide ranging as AGL, Telstra Health, RMIT University and Gerson Lehrman, Sarah has been at the forefront of bringing technology and ideas to market.

As Sarah says, one of the big challenges with data science is articulating its value. Data science costs money to develop, and data science costs money to run. So, why would somebody pay money for what you’re doing? In this episode, she shares some of her proven strategies for justifying to those outside of the data science team how the investments will create and add value.

The other great challenge that the data science team needs to grapple with, Sarah adds, is change management – how do you explain an application or product to people that have not used before?

Stay tuned as Sarah talks about how the data science teams can turn these challenges into opportunities.

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The end goal is to provide business users with trusted data, faster at lower cost. How do you build to achieve that? What approach should you take, which tools and what resources and skill-mix are required to support that?

Our Data Engineering Summit coming up on 31st August will look at exactly those demands. As we explore these challenges, we revisit a special recording from our Future Proofing Data Platforms event where our brilliant guests took a broader view and debated the pros and cons of centralised vs decentralised architecture.

In an interesting session Naveen Siddareddy, Data Solutions Architect at PlayStation advocated for decentralised platforms. Taking the opposing view was Rohan Dhupelia, Data Platform Senior Manager with Atlassian who championed for centralised platforms. Take a listen and see what you think of their reasoning and share your thoughts on which ecosystems are best suited to centralised vs decentralised structures.

Rohan will be joining us again at the Data Engineering Summit in person to offer up a retrospective on ensuring productive, adaptive, and resilient environments that are verifiable, less resource heavy and that critically produce greater outcomes. It’s going to be a fantastic event sharing lessons learned on building trust in the data that you provide and your role in enabling the business to succeed through data.

Thanks to our sponsor, Talent Insights Group!

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We’re thrilled to have Dr Aleksandar Lazarevic, Former VP of Advanced Analytics & Data Engineering at Stanley Black & Decker, joining us from the U.S.! He holds a wealth of experience and shares many of his hard-earned lessons in today’s episode.

Data is Alex’s passion and his journey in the data science field started 20 years ago with his PhD in Machine Learning. Throughout his career, he’s been able to evolve from someone with a solid technical background into a full-on data executive who’s able to drive real value across the company.

Alex’s proficiency with data, AI and ML, expands across industries. He has worked in healthcare, property and casualty insurance, banking and manufacturing. No matter the industry, building a data-driven culture within the organisation is always one of the things he strives for. His diverse background has granted him a unique perspective when it comes to approaching business problems using analytics.

Stay tuned as he shares his tips on identifying the areas within your organisation where applying data science can contribute huge value, and much more!

Thanks to our wonderful sponsor, Talent Insights Group!

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We are joined by Andy Sutton, General Manager Data Driven Transformation from Endeavour Drinks Group. Endeavour Group is the master brand that holds several well-loved Australian brands like Dan Murphy’s and BWS.

One of the benefits of the retail industry is the massive amount of data that is generated, not just from customers but from the operations side as well.

Andy stepped down from his previous role in Endeavour Group to dive straight into devising the organisation's data strategy and now serves as the bridge between the business and the analytics team. One of the key aspects of his current role is to identify priority use cases to work and focus on. When they started, 6 months ago, they had 275 use cases.

A vital component of their effort’s success was going back to the ‘why’. “Why do we need a data transformation team to exist? How is it going to operate? What is it going to do?” All these questions were the starting point of their journey and helped them identify the path they would follow. Instead of focusing on just building fancy models, they describe their mission as delivering real value through data.

A lot of their initial work was prioritising and identifying the biggest opportunity areas that would bring value to the customers and organisation. Out of their initial list of 275 use cases, they narrowed it down to four, three of which are being worked on currently, and the other one will be tackled in 2023.

Read the full episode summary here: www.datafuturology.com

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Woodside Energy has made great strides in leveraging AI for operational success and cost savings but getting to the point where the business was ready to invest was no easy feat. Lauchlan shares how to first gain investment in all frameworks so that you can successfully layer a decision making framework and roll out an AI strategy.

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In this episode, Felipe sat down with Aruna Kolluru, Chief Technologist for AI at Dell Technologies ahead of our Advancing AI Melbourne event on 6-7 April. She shared how they work on providing solutions for their customers and use all available technologies - AI, IoT, data, etc, - to reach their desired outcomes.

Aruna works with clients across a vast range of industries and this is a testament to the power data & AI hold to promote growth and generate business value, regardless of the area you work in. In her own words, data has become the core of innovation for every industry.

With the key role data plays in today’s organisations, making it accessible for the analytics team to extract insights from it is of the utmost importance. That’s why data platforms have become essential for providing reliable, quality data that can be leveraged to achieve business goals.

Tune in for the full conversation with Aruna on how to leverage data platforms. You can also join her in person when she presents with David Siroky, Asia Pacific Head of AI, Data Analytics & HPC, Director, Dell Technologies at Advancing AI Melbourne (6-7 April, 2022).

Huge thanks to our Diamond Sponsors, Dell Technologies and Microsoft for supporting this event for the data community - to learn more from them register here: https://www.datafuturology.com/advancing-ai

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Ant and Felipe sat down ahead of Advancing AI Melbourne (6-7 April) 2022 to discuss what executives need to be thinking about when it comes to advancing AI initiatives across the enterprise. In this candid chat they explore how artificial intelligence impacts humans and common fears around the machine. They also share on the opportunities with AI, where the machine can enhance human performance and free up time for more strategic work.

To hear more from Ant in person, register for Advancing AI where he will be on a panel covering: “Business readiness and engagement – navigating the path to business value.” Use code COMMUNITY for a special discount. https://www.datafuturology.com/advancing-ai-registration#aai-registration

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In this episode, Felipe caught up with Anuj Anand, CIO at Ausenco on his career path through the company from System Engineer to CIO. It’s a very real interview in which Anuj shares some very honest life lessons that have helped to get him to where he is at today. On presenting his first 5-year tech strategy to the board he was told that was definitely what they didn’t want to see! Undeterred he went away only to return and nail it! He shares some of the common challenges faced by CIOs today across everything from cybersecurity to recruitment. He also shares how AI is influencing their data strategy and the importance of building diverse tech teams.

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In this episode, Felipe is joined by three special guests and pioneers of data and analytics in Australia. In celebration of International Women’s Day 2022 we tackled the trifecta of tough topics in the data analytics community right now – teams, culture, and talent.

Often we hear that the data and the tech, even the analytics itself, are the easy part. People, engagement, and culture matter the most. If the shiny tools just sit on a shelf, with a disengaged audience then no one wins. What does it take to turn that around? Right now, we're exploring the limits of organisation-wide data literacy. Inside organisations, we want to change how decisions are made, but as yet it's not clear how much expertise people are willing to acquire as part of their day job. It takes a culture that will encourage and support that growth, teams that can flourish beyond siloes and the ability to find in-demand talent.

This show includes such a great discussion full of insight and real-world application.

You can catch even more from two of our fantastic guests, Michelle-Joy (MJ) and Kathryn, at our Advancing AI event taking place at the Crown Promenade Melbourne on 6-7 April. Register here to catch up with Felipe, MJ and Kathryn in-person: https://bit.ly/3HL8Iv1

Thanks to our sponsor Talent Insights Group!

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In this episode, Felipe is joined by Jason Mars, professor of Computer Science and Engineering at the University of Michigan. Also, an entrepreneur and an author, he cares deeply about creating an impact in the world. Describing himself as a tinkerer at heart, he has always tried to experiment and explore, with an ultimate focus on making a difference in the world. His background is in artificial intelligence at scale, having worked that on a large-scale computing system for many years. He has written about 100 research papers that worked with 14+ PhD students. Having started several companies, he has grown one technology company to a $200 million valuation bringing state of the art conversational AI to market.

In this podcast, Felipe and Jason discuss creating the kind of technology that can define the next 10 years of artificial intelligence and scalable systems.

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As the data analytics space matures demands from the business for the products and services being generated has been increasing. There is a thirst and appetite for internal and external data and analytics outputs. However, we need visible platforms and the ability to orchestrate complex data pipelines to enable this growth and underpin the mechanism.

Cue the emergence of the engineer. We started with the data engineer and have welcomed more specialised roles including the Analytics Engineer, ML Engineer and AI Engineer. Heavily leveraging from the IT side, these roles have evolved and are becoming integral to the business.

How exactly is the engineer in the data analytics space evolving? How is this enabling reliable accessible, performant systems that are monitored appropriately? Can we look at team structures? Are there ways we can implement or better use the modern data stack?

As an industry we’re maturing, we’re further specialising and we’re reacting to the demands of the business. We’re seizing the opportunity to create more value for our organisations!

That’s what Felipe will discuss in this week’s podcast episode.

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Scaling AI. There is a reason it's a hot topic for discussion, we all need to do it but it is fraught with challenges from the complexity of the tech itself to the people and processes needed to underpin scaling AI for production.

In this session we share the common challenges faced and address how to tackle those issues. We explore methodologies and approaches to successfully lift AI projects into production, the key architecture and tech needed, skills and talent required and finally, the processes needed to support all of this.

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For the first episode of 2022, Felipe speaks with Kari Jones, Head of Analytics and Insights at Countdown (Woolworths NZ).

Don’t miss all the gold in this episode as Felipe and Kari talk about leadership, analytics and more!

Enjoy the show.

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Don’t miss this week’s episode, as Felipe closes this three-part series for the Top 5 Data and Analytics AI Trends to watch for 2022.

Whether these data analysis and AI trends inspire you to brainstorm new models or update the existing ones in your toolkit, the choice is entirely yours.

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Felipe’s roundup of the top data analytics and AI trends for 2022 and beyond should give our creators a good idea of where the industry is heading.

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Don’t miss this episode as Felipe talks about the Top 5 Data Analytics and AI Trends as we move into 2022.

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This week, we are joined by Ivan Liu. Director of Engineering at Rokt. He specialised in software engineering, machine learning and cloud architecture.

Tune in for the full conversation where Ivan talks about ML ops and ML Engineering.

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In this week’s podcast episode, Felipe spoke with Chris Dowsett, Head of Decision Science and Analytics at Spaceship.

Chris has been working in Data Science, Analytics and Decision Science for the past 18 years. His specialty is helping business leaders make decisions and solve business challenges using data science and analytics.

Don’t miss this episode as Chris talks about the difference between data and decision science, plus much more!

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For the first time in Data Futurology podcast, we’ll be having a two-in-one episode.

In the first part of this episode, Felipe Flores is interviewed by AI today podcast hosts Kathleen Walch and Ron Schmelzer.

Cognilytica's AI Today podcast focuses on relevant information about what's going on today in the world of artificial intelligence.

In the latter part of this episode, Felipe interviews Kathleen and Ron to talk about certain challenges and finding out the real potential of AI.

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In this week’s episode, we are joined by Maggie Shi, Head of Solutions Engineering Facebook ANZ, Japan and Korea. Maggie combines her 20+ years of software development, technical directing and project management experience into her current role at Facebook. She has a strong background in Java/C++ development which she has used as a foundation to lead strong technical teams.

Tune in for the full conversation where Maggie deep dives into privacy-preserving and data sharing.

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We are joined by Florian Douetteau, a legend in the industry, who built a unicorn company within the data space. He is the CEO of Dataiku, a platform that assists in systemising the use of your data for exceptional business results.

Florian is a mathematician by trade, however he found the mathematics world too theoretical for his liking and wanted to move into a space where he could work on practical applications of the discipline.

He realized one of the things avoiding the widespread use of business intelligence was the fact that whatever the advance in the reporting, data itself was a problem, and in his own words, when you’ve got a problem and you want to solve it at scale, software is a good solution. That’s where Dataiku comes in, through their platform they enable teams to create and deliver data and advanced analytics using the latest techniques at scale.

Their motto is “Leave no one behind with AI.”, which Florian explains comes from the fact that for a data & AI project to have the right impact, you need a balance in terms of who is behind the wheel. It shouldn’t be just data scientists, you also need business stakeholders to participate. Ultimately, Dataiku is all about making data science and AI accessible to all the people in the organisation and enable them to take part in building AI and consuming AI-driven applications.

Tune in to hear more about Florian’s start in the data space, the lessons he has learned along the way and how he is working towards the democratisation of data through his company, Dataiku.

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In this episode, we speak with Chris Wexler — CEO of Krunam. Krunam is in the business of removing digital toxic waste from the internet using AI to identify CSAM and other indicative content to improve and speed content moderation. Krunam’s technology is already in use by law enforcement and is now moving into the private sector.

In this sensitive and equally essential topic, we’ll learn from Chris why AI is the future of fighting child sexual abuse material.

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In part 2 of our interview with Steve Nouri, he spoke about AI specializations and share his thoughts about AI innovation in developing countries.

He says that AI is quickly evolving and we’re going to see a lot of specialized people working in the AI field in bigger organizations.

Also in this episode, we discuss personal branding and its importance for a Data Scientist. Steve shares first-hand experience on how he got started with his personal branding years ago and how it helped him with his role at the Australian Computer Society.

Don’t miss this episode as Steve shares his wealth of information with us.

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We are joined by Steve Nouri, Head of Data Science & AI at Australian Computer Society and member of Forbes Technology Council. With 450k+ followers on LinkedIn, Felipe calls him “LinkedIn’s King of AI”.

Steve’s background is in software engineering but he got interested in data over 14 years ago. He went on to get a masters degree in Data Analytics and then transitioned into the data field completely.

In his role as Head of Data Science & AI at ACS, he encourages and helps people interested in the data world to pursue a job in the field. He is passionate about giving back to the community and he explains where his passion for data science comes from. Steve knows the potential AI has to create a huge impact in society, whether it be positive or negative. That’s why he dedicates so much of his time to work towards the ethical use of AI and to advance the applications of this technology for the greater good.

Tune in to learn everything about Steve and the impact he is having in the AI community through his many roles at Forbes, ACS, ISO and more!

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Organisations are in constant need of innovation to fulfill their end consumer’s needs. That’s why several companies have started innovation areas to help update and grow their main business. Today we are focusing on starting a Machine Learning and AI initiative within these innovation areas to grow the value they are providing, scale their results and positively impact the rest of the organisation.

We are joined by Ram Radhakrishnan, General Manager for Customer Analytics, AI & Data Science at WooliesX, where he is building world-class capabilities thanks to 250+ data practitioners coming together. Ram is certain that AI and ML are future capabilities able to take organisations to new heights, whether it be solving business problems or providing a unique customer experience.

The AI & ML space is quite big and diverse, but both Ram and Felipe share how they’ve seen a huge rise in the demand for machine learning engineers due to the growing need of scaling and operationalizing models.

When it comes to hiring, the most important skill he looks for in candidates is the right attitude and the desire to always keep learning. WooliesX has a focus on teams and their members and works for them to dedicate nearly 20% of their time towards developing their own skills and knowledge.

Tune in for the full conversation where Ram goes into detail about WooliesX agile practices, their scope of work, growing their teams and what he believes are the most important upcoming skills in the data space.

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In this episode, Felipe speaks about what is going on with artificial intelligence in the retail space. Visual search and recommendations are currently trending in retail. Companies are offering the ability for consumers to search based on a picture. For instance, consumers can take a picture of a pair of shoes and find that product online, similar products, and products that would go well with it. Humans can process images way faster than text. Plus, pictures carry more information than sound. Visual search and recommendations are hot and on the rise right now.

Free shipping and free returns are hurting companies. People are buying multiple items with free shipping and then returning it. As a result, the retailer is paying for transport on both ends. Retailers are struggling with this model. However, free shipping is something that consumers are looking for. So, brands are offering a subscription model or a rental model. Sometimes, a combined subscription and rental model. More and more companies are offering clothing rentals.

Next, Felipe explains what to expect in retail. Automated stores are popping up in the United States from Amazon. You pick what you want in the store; then, you walk out without having to see a cashier. With advancements in AI, the movements of customers can be tracked in retail stores. Voice assistance is also something you can expect to see in the future of retail, like ordering something using an Alexa. Stay tuned as Felipe speaks about the basics of AI in retail.

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Christoph Molnar is a data scientist and Ph.D. candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. Christoph is passionate about using statistics and machine learning on data to make humans and machines smarter.

In this episode, Christoph explains how he decided to study statistics at university, which eventually led him to his passion for machine learning and data. Starting out studying with a senior researcher gave Christoph exposure to many different projects. It is an excellent program for students and companies whom both benefit greatly. Christoph learned so much about statistics that he would not have been able to acquire otherwise. The clients got nine hours of consulting for free, which is very valuable for their businesses. When Christoph started his statistical consulting career, he did patient analysis to assess if a medication was affecting the spine. He found this very interesting as it differed significantly from his previous consulting.

When labeling data, Christoph says to label and always compare continuously. For instance, when a student labeled one photo, later on, Christoph would show a student the same photo and see if it got labeled identically. Sometimes people will see the same image but label it differently; so, this is one thing you can do to ensure labeling data is going smoothly. If you have multiple labelers, you will need to compare how each labeler will mark the same photo. Do not be blind to the quality of your data; it is easy to adjust the numbers.

Then, Christoph speaks about pursuing his Ph.D. in Interpretable Machine Learning. He publishes his book, Interpretable Machine Learning, on his website chapter by chapter. Christoph gets feedback and uses it while continuing his writing on future chapters. Learning about interpretable machine learning is not exactly present at university now. Some schools and professors are starting to integrate it into the curriculum. Stay tuned to hear Christoph discuss accumulated local effects, deep learning, and his book, Interpretable Machine Learning.

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Steve Monaghan is the Chief Digital Officer at Riyad Bank. He has a series of investments in banking and insurance using AI and new technologies in order to improve efficiency.

He started out as a commercial pilot working 22 hours a day, running the aviation company during the day and flying at night. His first contact with data came when he started using spreadsheets to automate tasks and accelerate the time in which he got them done. He was able to quote 3 days before any of his competitors and his business boomed. This gave him a great insight on the impact technology can have.

His career then moved from flying into technology. Within this industry, he has learned that people have a learning curve and for them to adopt change, they first need to understand it. You need to show people how processes and systems work for them to understand and accept them. That’s the reason why he thinks curiosity is such an important trait.

For Steve, the three core laws of technology that drive data science and AI are:

Moore's Law: processing power doubles every 18 months for the same cost.
Metcalfe's Law: the value of a network grows by the square of the network's size.
Kryder's Law: storage doubles every 13 months for the same cost.

He believes these 3 laws encompass the ability you have to assimilate learning into knowledge. The faster you do that, the bigger the advantage you have over your competitors. It’s almost as if you live in their future because you’ll be able to see things they won't see for a period of time.

Stay tuned to learn more about the impressive way he looks at what you can do with technology, the results he’s had and how he leverages AI in business.

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Stuart Long is the CTO Hewlett Packard Enterprise. In his role, he identifies different product portfolios and pulls together solutions for different customer sets, such as public health, education, finance, retail, mining, and determines how to develop the solutions from HP’s product portfolio across all the different stacks.

In this episode, Stuart tells us how HPE are moving the analytics to the edge, making it actionable with the ability to link data across the organization and across multiple organizations with a new solution out of Europe; Gaia X. He says that with today’s privacy regulations, it’s now about federating your data and then making that data available to people but still within your control. But you can’t do it alone – you need an ecosystem of partners and products to do that. And that’s exactly what HPE has done.

They have built a whole range of products to do what they call ‘federated data analytics’. This allows you to look where your data is and start to then understand where you want to analyse that data, and what data flows you need to provide, and how you want to basically store and categorize that data. There are a number of what they call “architectures”, and underneath those architectures, they are developing products that fit nicely within those to enable the customers to look at how to deploy these new systems.

Finally, Stuart tells us what excites him most about the opportunities ahead for Gaia and the data economy as a whole and HPE’s plans for Gaia in the next 3-5 years. He says the ability to see a whole new range of services be made available very quickly, and different ways of utilizing those services - is what he calls ‘service chaining’. Giving the consumer a way of being able to choose from multiple different providers and customize their own different services and chain them together.

Stuart believes it’s really about developing the whole ecosystem and bringing on different partners. We will start to see more edge to quarter cloud type environments where organizations will now have a lot more edge processing. Companies will become more and more digital, and services will become more and more digital. He tells us that there will be some interesting opportunities for organisations and with this level of innovation, there is going to be good and bad, but it’s about making sure that you can optimize the good and limit the bad.

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Organisations work hard on creating and building data science capabilities and models that will provide a real positive impact for their users. However, the feedback we’ve had from the Data Futurology audience shows there is a struggle to get ROI from their AI. So what do you do when your models and AI efforts are not living up to their promises? This conversation explores how to ensure that your AI projects don’t fall flat, but actually deliver business value.

In this episode, we hear from Scott Hubbard, Director of HPE Ezmeral Software Business Group, South Pacific and Ridhav Mahajan, Solutions Engineering Leader, HPE Ezmeral Asia Pacific. They have recently been part of setting up a start-up within HPE, that focuses on cutting-edge stacks in the AI, ML and data space. Though this startup is fairly new, the technology behind their platform, Ezmeral, has been in production for over 10 years now!

One of the current challenges organisations face is accessing all of their data. Whether it’s stored in silos in the cloud or on-premises. As data grows exponentially, being able to scale a platform almost infinitely to keep up with that demand is a vital aspect. To address this, they have worked on what they call Data Fabric, a layer under a data platform with the ability to unify data sets across an organisation and bring data silos together so you can have a single data source.

Tune in to hear Scott and Ridhav expand on the adoption of AI in an organisation and share their industry experience with us.

If you want to learn more about leveraging your data with AI, join the HPE Ezmeral Asia Pacific Launch to hear our host, Felipe Flores, share his insights on Getting Value From AI At Scale. Save your spot here!

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If you are trying to choose what skills you should build as part of your career, no single answer fits everyone. It depends on your strengths and ambitions. Your strengths are the things you are naturally good at. If you're not sure what your strengths are, then think about the things that people come and ask you about. When someone asks you how you do something, most likely that is one of your strengths.

Also, think about where you want to end up in your career. For instance, if you're going to become a manager, find a way to be a leader on a project and develop your soft influence. Hard influence is something that comes from authority. Whereas soft influence is something that we can build upon. Find a way to lead a project among your peers. Taking the lead will allow you to practice your influence, leadership, and management skills. A group of people will follow the person with an organized and well-thought-out plan; be that person!

Another tip - do not be the person that blindly follows what they enjoy; it's not a strategic way to go about crafting your career. Felipe has seen people have a successful career doing work that they enjoy. However, they will eventually lose their love for it. Plus, if you only follow what you enjoy, then you run the risk of getting pigeonholed in an area that may not have future growth. Also, it might be an area that loses demand and importance; it can make your skills irrelevant.

At the end of the day, we need generalists. Companies need people who are knowledgeable in the end to end process. As an industry, data scientists are in high demand. Plus, they need people who have a mix of skills. There are always more roles being added because the industry is starting to understand that a great deal of knowledge is required to build a successful data science team.

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Read the full episode summary here: Ep 108

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In part 2 of our interview with Abhi Seth, he tells us that part of his role is to really drive scale for analytics across the ten businesses within TE. We learn how the adoption of analytics is enabled throughout the organization and a big component of that is driving, understanding and building capability within the Centre of Excellence (COE).

He says the first 90 days in the COE is about building capability and being able to have experts in data science, cloud, dev ops, ML Ops, data visualization, user experience, storytelling, and data engineering. Abhi says his focus is now on building a small COE team within each of the businesses and moving to a “hub & spoke” model as the analytical maturity of the organisation improves.

Abhi goes on to tell us about how he creates and enables “seed teams'' and how it’s important to ensure the problems you're solving are creating value for the company and are tied to a strategy. He also says you should have a committed executive sponsor.

Throughout the episode we discuss the results of our poll questions:

Does your organization have a data science or analytics Center of Excellence?
How is your organization's cloud migration going?
Does your organization centrally manage the delivery of analytics across the enterprise?
Does your organization measure the success of the analytics function?
Does your organization develop their analytical talent?

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Read the full episode summary here: Ep 163

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Abhi Seth started his journey as an industrial engineer which he describes as his passion, and then found his way into research. His PhD is in mechanical engineering, and human-computer interactions, which he says was kind of a double major PhD.

Abhi started working with virtual reality applications for mechanical engineering and says back then it was about how to simulate environments and models where you create a suspension of disbelief for a user. So he set out to prove that VR was also valuable in solving real industrial problems which led him to join the Global R&D centre for Caterpillar, the world’s largest mining construction equipment manufacturer.

Abhi currently leads a global data analytics team for TE Connectivity, a $14B electronics manufacturing company. He says he is always up for a challenge and learning something new, one of the reasons he decided to join TE Connectivity.

We learn from Abhi about the purpose of centres of excellence, the differences between data science COE, and a centralized department and how do you measure success with analytics.

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Read the full episode summary here: Ep 162

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In part two of the Ultimate Uplevel for Business-focused Data Professionals, with Lillian Pierson, we learn about her data action strategy plan. She created a data evaluation use case workbox with 31 use cases broken down by industry and function and tells us that if you are innovative then it's actually everything you could possibly need. You don't need to read 100 use cases.

Lillian says you should survey industry use cases to find what's possible. Take stock of your company, look at where the biggest gap for a data solution is, and then assess possible options against use cases. Aim for projects that will make an impact within 3 months.

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Read the full episode summary here: Ep 161

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We are joined by Lillian Pierson, a very well-known figure in the data space. She started her own business as a data consultant around 2013 and eventually started helping others become better data leaders and entrepreneurs. Currently, she has helped train and educate over a million data professionals! Lillian is not only a great data business mentor, she is also the CEO of Data Mania and a data product manager.

She touches on the exponential growth the data science industry has had in the past few years and shares how hard it was for her to get useful data content online when she first started. That’s part of why she is interested in helping others and making knowledge available to everyone.

Quotes:

"I've been able to reach and help train and educate over a million data professionals. I turned the business on its face a bit, I have a community of 650,000 data professionals. So instead of working as a data consultant, I started helping other data professionals become better data leaders and entrepreneurs. And so that is, that has been the core focus of our business for two years, and I'm committed to that not changing in this business ever."
"You know people can learn how to be successful, they can learn how to create a successful product, how to run a successful business, how to make a difference in their career. So you show them the processes and the steps that they need to take in order to do that."

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Read the full episode summary here: Ep 160

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In part 2 of this episode, Nitish shares his thoughts on how to ensure that the data scientists expertise are well utilized. He says their key metric last year for the entire team was to hit exactly one number - 100 active users for their data platform with only 50 data scientists! He also says organizations should focus on enabling self service across the business.

Nitish talks about the data support channel at Afterpay which allows for anyone to ask questions about the data and the data platform. He says find the people naturally drawn to the data and support them with assistance, training and coaching.

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Read the full episode summary here: Ep 159

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When Global Head of Engineering, Nitish Mathew looks back at his journey over the last four years with Afterpay, he describes it as a phenomenal ride and a tremendous learning journey. They have had to reinvent themselves three times in a very real way, getting rid of their first and second platform and building with the view of not assuming that this is for the next 10 years, but building with the view that they will need to get rid of this in two to three years.

Nitish says when a data scientist joins a company they should be able to query a data lake or data warehouse like any library. We need to help users find information quickly so they can actually get the information that they want to do their research immediately. It needs to be organised so people know how to find what they need.

We end part one with a discussion on how organisations should use coaching to get the best results. Ultimately, leadership's role is to get the right outcomes and support people with coaching, technology, decision making and resources. He believes the goal is not to build the fastest product. The goal is to make sure your data scientist colleagues are happy. And for them to be happy, you need to make sure that she or he is able to do their job fast, which involves giving proper easy to use performance tooling, giving good data, and then making sure that on a daily basis, it works.

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Read the full episode summary here: Ep 158

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In Part two of this episode, Nonna talks about the role culture plays in a data-driven organization and the importance of role accountability and the support of leadership.

One of the things that Nonna strongly subscribes to is the RMIT vision to support their students in every possible way including a new initiative called the Data Innovation Hub which gives students 12 months of real industry experience. Nonna says their key focus at RMIT is to make sure that their students are ready for life and work.

Also in this episode, we discuss why a widely known and explicit data strategy is vital to every organisation. Nonna says that everything is about data, and it doesn't matter what your organization is or does, because, without data, it's not going to be successful.

In this episode we discuss:

How using interns can benefit your organization
Data Owners vs Data Trustees
Working with peers and data leaders across an organization
The use of data analytics during Covid-19
Do executives trust the metrics

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Read the full episode summary here: Ep 157

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Born in Moscow Russia, Nonna got her first degree in Engineering but after emigrating to Australia, soon realised there were few engineering jobs around. Nonna says she had no choice but to do more study and decided on a master’s degree in project management from RMIT.

Nonna has spent the last two years at RMIT building a data and governance strategy. In this session, she shares her journey, as well as how they set about establishing a culture for data governance.

The RMIT journey covers everything from agreeing definitions, certifying reports and using data trustees. She also shares how to build happy teams and how through partnering with the university, they are better equipping graduates from the university with practical experience for workforce-ready employees. Tune in to hear more on her journey!

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Read the full episode summary here: Ep 156

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In Part 2 of getting to know Felipe Flores, Felipe answers questions on what types of environments and challenges keep him energised, where he sees the data science industry heading in the next few years, and how job seekers can de-risk decision making for their potential employers.

Felipe talks about:

Using data to improve Data Futurology
The gap between University and industry
Which projects should you show to potential employers to demonstrate good foundational knowledge
Intellectual capital vs money capital, Angel Investors and the approach to start and develop a business
Strategies and decision making techniques to adapt to change
MLOps – Felipe’s view on using Feature Stores when deploying models

Read the full episode summary here: Ep 155

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In part one of this special episode, Felipe reflects on the path his career has taken from his first start-up company to how he started Data Futurology and to his latest role in Healthcare. In this open and honest conversation, he reveals the mistakes he made along the way and the lessons he learned from those failures. Successful people do have struggles, but you must be willing to ask for help.

Passionate about his field, Felipe began the Data Futurology podcast in 2018 when he realised he needed a productive outlet for his obsession! Fast forward to today and he has interviewed over 200 guests from Australia and around the world from companies such as NASA, Formula 1, the AFL. And even a futurist!

Listen in next week for part two as Felipe takes Q&A from the audience.

Quotes:

There were so many things I did wrong during that time and so many things that I learned during that time, but it was the best training ground because the stakes were so high, and I felt like I had to be improving so quickly. It taught me a lot of lessons and a lot of what has helped to well in corporate is the lessons from my time as an entrepreneur.
The first year was tough. There was a huge education piece. Nobody understood data science. Nobody really cared about data science. I would go and speak to business heads and executives let’s talk about what we can do for your area. But people did not take it very seriously.
I’ve interviewed over 200 guests, including the CTO from Nasa, Martin Ford a futurist, that was super exciting. We have had people on the show from all different industries. It’s been awesome.

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Pieter Vorster is the GM CSR & Chief Data Analytics Officer at Commonwealth Bank, one of the largest companies in Australia. He is an internationally recognized leader in the data analytics space with over 20 years of experience in industries like retail, wealth, corporate and investment banking, financial services, insurance, etc.

In this episode, Pieter explains how he considers his real data journey started in 2013 when, with 2 colleagues, he had the opportunity to travel the world and meet with people who could help them in applying digital data design into corporate banking. Then, he talks about how disruption within the organizations, though sometimes avoided, is necessary to create better solutions for the customers. He believes a key guiding principle should be to benefit the client, and that transforming strategies and technologies can only take place at the same pace as social transformation.

He provides his insight on the roles of CDO and CAO, both are positions he has held at different companies, and how he sees that these may evolve in the future.

Stay tuned to learn more about Pieter’s exciting journey and his ability to combine customer perspectives with data, AI and digital business models in a unique and effective way to get results for organizations.

Quotes:

“The moment you need to get agreement from everyone, mediocrity around consensus will apply”
“If you’re just trying to move money from one part of the business to another part of the business you’ll never succeed”
“I tried to convince some actuaries to learn about computer science and programming”
“The banks have got lots of data, lots of toolsets, people just don’t know how to use them”
“Logic never wins arguments, emotions win arguments”

Read the full episode summary here: Ep 128

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The digital transformation of the retail industry has been going on for years. It has increased speed, efficiency, and accuracy across every branch of retail business, thanks in large part to advanced data and predictive analytics systems that are helping companies make data-driven business decisions.

None of those insights would be possible without the internet of things (IoT), and most importantly, artificial intelligence. AI in retail has empowered businesses with high-level data and information that is leveraged into improved retail operations and new business opportunities.

In this week’s podcast episode, Felipe speaks with Aaron Pratt. AI & Advanced Analytics Lead at Country Road Group.

Aaron is currently the AI & Advanced Analytics Manager at Country Road Group and David Jones.

He loves to uncover what it is that makes customers tick, and strives to use his data science expertise to provide the best possible customer experience to them. Having started his career life as a Games Designer with very strong mathematical prowess in the Gaming Industry, he quickly came up with a range of creative ideas based on a range of statistical models.

This has led to a variety of AI use-cases, from facial recognition time-carding to fraud detection to omnichannel personalization, now implemented throughout a number of different retailers.

Quotes:

NPS is probably one of the key things that we, amongst many other organisations to start to understand sort of customer sentiment.
Definition for price optimization. Basically, if you're selling a product, the one thing that you can control is the price point they sell it at. So if you're selling, say a jumper, or a T-shirt, or like whatever you're selling, the price point directly, is impacting the demand for that product.
So the demand for the online like, when we went through COVID, I'm sure we weren't the only ones. But online just went absolutely through the roof.
I think AI, relies on the BI to be right. Most of the time. So if you're wanting to get AI really embedded correctly, you need to make sure that you have your BI completely sorted out.

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Read the full episode summary here: Ep 153

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As few as 15% of data scientists today are women. And that lack of diversity is a serious issue. AI algorithms are susceptible to bias, so building them requires a team that includes a wide range of views and experiences. What’s preventing more women from joining the field?

In this week’s podcast episode, Felipe will be speaking with Fiona Milne, Head of Data and Ai at Whispir and Founder of WiMLDS Chapter

Fiona is an Astrophysicist and Mathematician that heads the data and Ai at Whispir. With more than 10 years of experience in data science, Fiona offers Whispir invaluable experience on machine learning with previous projects for NAB, Odds.com.au, MYOB and the ACCC (Australian Competition and Consumer Commission).

She has also founded the ‘Women in Machine Learning and Data Science’ Meetup alongside two industry colleagues. The group encourages and supports women in the industry through monthly events which include speakers, panels, evening and lunchtime events.

With a Masters in Astrophysics, she also has spent time working on ‘passion projects’ which use machine learning to at images from Kepler telescope and leveraging Google AI’s open-source library Astronet to potentially discover new exoplanets.

Quotes:

Diversity is really important. Diverse teams are proven to be more creative and more innovative. And they're proven to be better at solving problems, they're proven to be better at making decisions.
Biases or unfairness in the development of AI. And that kind of like helps us have a conversation where we get to decide the society that we want to create in the future, instead of blindly perpetuating the one that we've had so far. Knowing that there are improvements we can make there.
If you are in a position to vote with your feet, go for it.
We need visible leaders that come from different parts, that think differently, that is diverse in their genders and across the spectrum.

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Read the full episode summary here: Ep 152

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In this week’s episode, Felipe will be speaking with Simon Herbert, NSW Customer Service Department’s Chief Data Officer.

Simon has worked for the NSW Data Analytics Centre for over three years. He has been instrumental in the transformation of the DAC to an agile culture.

He and his team have built an advanced analytics service in the commercial cloud which delivers the enhanced capability and scalability to support the Customer Service department and NSW Government as a whole.

Simon has over 20 years of experience in data, technology and transformation in many different countries including the UK, US, Singapore and Hong Kong. He has worked for companies such as Macquarie, Westpac, HSBC, IBM and Motorola.

He was recognised as one of the top 20 CIOs in Australia as part of the 2019 and 2020 CIO50 awards.

Quotes:

It's very important that you have data governance outside of data science or data services, there is a Chinese wall to make sure that a steward can actually go up and say, no, you can't do that. The privacy Impact Assessments did not occur, which we cannot release. That's really important to have those kinds of controls in place.
They have such a deep understanding of our behaviour and the way that they do that they're testing first their control groups and their rollout. But it's very scientific, and they can make a significant impact on a number of outcomes throughout the state.
And so the application of deep learning definitely can give some significant benefits. But you do need to make sure that everybody understands the benefits. You haven't put any bias in and all the other things, we need to be very careful around deep learning.

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Read the full episode summary here: Ep 151

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Today we have Apple’s Senior Data Scientist, Farhan Baluch as we deep dive into Data Analytics.

Farhan has over 10 years of combined academic and industrial experience in machine learning. He likes to combine strategy with tactics and enjoy operating both at the micro and macro levels of data science efforts.

As a neuroscientist, he worked on data obtained from sensors used to record neural activity from humans and insects. As a data scientist in the business world, he worked with data from large automotive, telecommunication and other industries as well as led small teams of data scientists at Opera solutions. In his role at Netflix, he brought data science to the world of entertainment and used quantitative methods to bring structure to the problem of predicting the success of content on Netflix.

Currently, in his role at Apple, he’s working on a variety of projects aimed at personalizing the search ad experience within the App Store.

Quotes:

When I talk about effective data science, and we're specifically talking about data science, which is the combination of sort of machine learning, math, statistics, and in the business domain knowledge, it's purely doing sort of machine learning and AI for a particular purpose, which is to add value to your business. And so when I talk about effective data science, it's really about seeing in that sense, how do you come up with a process or a data science function that brings that value to your business.
In terms of effective data science, I think you want each function to be doing, what their domain expertise is, and you want as little sort of cross-functional cooperation. Although not necessarily people leaning over onto the keyboard, right? And I think that's where the effectiveness comes in.
I think AV testing gives us that power to sort of scale customer feedback, without having to actually do interviews, though, anecdotal interviews do help.

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We are joined by José Murillo, Chief Analytics Officer at Grupo Financiero Banorte, Mexico's second largest financial group and also the most profitable one pre-pandemic.

José had a long tenure at Mexico’s Central Bank, where he worked at the research department. Around 7 years ago, he joined Banorte and established the Analytics Business Unit. His efforts delivered profits equivalent to 46x the cost just within the first year and almost 250x their cost last year. During his time there, he has helped the bank go from the fourth to the second largest financial group in Mexico, surpassing companies like Grupo Santander and CitiBank.

Quotes:

"The idea was to build this data science team and I think it was a blessing in disguise at the time, in the sense that they funded the group, but I think there were still some doubts of what would be the yield of bringing people different from what the group used to have. They said "Yes, we are going to fund it but in a year time you need to deliver 10x your cost. It seemed quite steep because for a traditional business they were asking 3x the cost. Long story short, within the first year we delivered 46x the cost and last year we were close to 250x our cost."
"We were built to deliver profits and I think that helped us focus on things that were valuable to the organization and to be accepted because we were bringing value."
"I’ll be honest, I was a bit overwhelmed when they told me that I needed to make 10x my cost in a year and I was a bit reluctant to accept the challenge. My boss told me ‘you’re not going to have a problem, just go and look at the amount of resources we have on the credit card business. You’ll be fine.’"

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Read the full episode summary here: Ep 149

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Today we are joined by Doug Laney, Data & Analytics Innovation Fellow at West Monroe and Author of "Infonomics".

Nearly two decades ago, Mr. Laney originated the field of Infonomics, developing methods to quantify information's economic value and apply asset management practices to information assets. He authored the book "Infonomics: Monetizing, Managing and Measuring Information as a Competitive Advantage," and lectures at leading business schools on the topic. In addition to his dozens of Gartner research publications and blogs, Mr. Laney is a contributing author with Forbes and Information Management Magazine, and has been published in the Wall Street Journal and the Financial Times. He also edited and co-authored Forbes' e-book on Big Data.

He was a vice president and distinguished analyst with Gartner's Chief Data Officer (CDO) research and advisory practice. He is an accomplished practitioner and recognized authority on data and analytics strategy, and is a three-time recipient of Gartner's annual Thought Leadership Award, and is regularly considered one of the top global influencers these topics. Mr. Laney specializes in and assists organizations with data monetization and valuation, open and syndicated data, data governance, and big-data based innovation. In 2001 he coined the "3Vs" of volume, velocity and variety, now commonly used in defining Big Data.

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We have Kevin Buckley, Founder and CEO of Torrey Pines Law Group, PC in San Diego, USA. He joins us for an insightful chat on Life Sciences and AI.

He created Torrey Pines Law Group when he returned home to San Diego in 2013. Throughout his career, he has successfully represented pharmaceutical, biopharmaceutical, medical device, biotechnology, specialty chemical, digital health, healthcare software, artificial intelligence, and convergence technology clients for more than 20 years.

Quotes:

"If you got a patent on something you can’t detect the infringement of, then why get a patent in the first place, maybe keep it trade secret or as a small subset of trade secret law. Early on you need to decide what you want to do."
"IP does not exist in a vacuum. IP affects so many different other areas of law, business and science to some degree. Also, different laws, business and science also impact IP law, so it is completely integrated with every other facet of law."

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We have Calvin Ng, Head of Equality and Linking at Equifax and Patrick Choy, Senior Consultant at Talent Insights Group. Today we are focusing on teams. Everything from hiring, how to structure them, retention and more useful tips.

Patrick shared some tips for data scientists who want to differentiate themselves when going through recruitment processes. For him, communication is key and he encourages people to communicate their past successes and show the recruiters how they could use that knowledge and experience to create a positive impact for their business.

We touched on how some jobs may be advertised as data science roles and once hired, individuals realize the work needed to be done is not what they expected or part of the data field. Companies don't always have a good handle on exactly where they sit in the data maturity curve and many of them are still very much starting their data journey. For Calvin, this is an exciting opportunity for professionals who are willing to take on the challenge of helping them create their data science teams and structure from the ground.

As individuals continue to upscale their skills and organizations try to involve more data science in their processes, the field and competition in it will grow, so stay tuned to hear insightful tips from Australia’s leading data specialist recruitment business, Talent Insights.

Quote:

The high performing team is the team who trust each other, learn from each other and has a diverse set of skills and viewpoints.

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Doug Farell is the Senior Web Engineer at Shutterfly and Author of The Well-Grounded Python Developer, he joined us as our first live webinar guest back in January.

Doug has more than 20 years of software development experience in several industries and disciplines. Having been in the industry for many years, Doug fully embraces the concept that "change is the only constant" and can take advantage of emerging technologies and demonstrate the benefits of new technologies to co-workers and management.

He has a real passion for technology and that becomes evident in his book The Well-Grounded Python Developer.

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We are joined by Alexey Grigorev for an episode that will be very useful for anyone wanting to identify the skills needed to get into the data science world.

Alexey works as a lead data scientist at OLX Group, where he deals with content moderation and image models. He is an experienced software engineer with 10 years of experience. In the last 6 years, he focused mainly on different aspects of machine learning. Previously he successfully competed in many data science competitions and wrote a few books about machine learning. One of them is Machine Learning Bookcamp — a book for software engineers who want to get into machine learning.

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We have Matthew Dixon, Assistant Professor of Applied Math at the Illinois Institute of Technology and author of the textbook, Machine Learning in Finance: From Theory to Practice. He has also written several journal papers on algorithms and models for machine learning, blockchain based technologies with applications in fintech.

Quotes:

"Do you want uncertainty as your first class citizen or do you want it more as an afterthought?"
"You had to fit models to the data. I realised quickly that was the achilles’ heel for the approach."
"It isn't just a guessing game."
"In the Bayesian world, it sort of turns everything on its head. It says every parameter in your model is a source of error."
"I think interpretability is a must. Not only to appease regulators or non technical finance professionals but rather when something goes wrong."

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Merve Hickok, Founder of AIEthicist.org, joins us today to share her knowledge on AI Ethics.

She is an independent consultant, lecturer and speaker on AI ethics and bias and its implications on individuals & society. She creates awareness, builds capacity and advocates for ethical and responsible AI while also collaborating with several national and international organizations building governance methods.

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Read the full episode summary here: Ep #142

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We are joined by Michelle Perugini, she is the co-founder and CEO of Presagen and Life Whisperer. In 2017, she had the opportunity to combine her passion for healthcare and technology and started Presagen, an amazing company that's doing things that actually change people's lives.

Presagen is both a platform and product company. They have a really unique AI platform that helps them build scalable AI medical products. One of those products is Life Whisperer, which uses AI for the embryo selection during the IVF process. Instead of having an embryologist looking down a microscope at your embryos and doing a visual assessment, this gives them the ability to use AI as part of the decision process.

Quotes

If I didn't happen to have that opportunity I may never have taken that step and wouldn't have this amazing career path that I've been able to have and that I'm really passionate about.
We were young, we had no commitments, we didn't have a family, so we thought why not? If we're going to do it, might as well do it now. So we did.

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In this episode we talk to Tyler Folkman, Head of Artificial Intelligence at Branded Entertainment Network.

Tyler joined Branded Entertainment Network almost two years ago and ever since has been working on how to leverage AI and data in the world of entertainment, specifically product placement. Their work is similar to that of a recommendation algorithm in that they use data to connect brands with the right creators to offer their audiences relevant products. They help brands find the best partner possible across different platforms like TV, YouTube, social media, etc. His main goal is creating value for the company with data and he has built a team that shares this belief with him and helps him make it possible.

Quotes:

"I really am a big believer in data scientists being able to own the process, all the way from data collection, cleaning, modelling, to deployment and even creating web applications. The data scientists on our team can deploy their own models and create front end interfaces so that someone on the team can come and play with the model that is not a data scientist can see what the predictions are and see if it makes sense. And we found when creating a platform for data scientists to do that, it allowed them to work much more efficiently. "
"I have seen people fail sometimes if you treat it more like an assembly line where data scientists only model, they just get blocked too much and they have to rely on so many teams to get value. It just never works in my experience."

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We have Carlos Avello, Marketing Science Lead at Amazon (Director of Marketing Analytics at eBay at the time of recording) joining us for an insightful chat on Causal Inference and how it can help businesses understand causes and impacts for better decision making.

Even before ecommerce started, Carlos was already working within an extremely data-driven business model. He started his journey in direct response marketing, tracking data from coupons and later analyzing it.

In his own words, he explains causal inference is based on evidence you collect of the impact a certain marketing effort had on your overall sales. Experimentation is the core of causal inference, and specifically A/B tests are used very often.

Quotes:

"Causal inference is based on evidence you collect of the impact a certain marketing effort had on your overall sales."
"Experimentation is at the core of it. You cannot get into causal inference if you do not compare two different populations with different treatments."
"Marketing is the product of a partnership between the advertiser and the advertising platform. Two different companies and there are things that we are ready to share and some others that are tricky to share, like customer data. If you want to setup a test where you are suppressing treatment to some people and allowing others to be exposed to the treatment and then compare the final results on sales you need to find a solution were you are saving enough information so the advertiser knows who has been exposed and who has not and later they can compare the sales."

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We are joined by Aurelie Jacquet, Chair of Standards Australia, an independent not-for-profit organisation that specializes in the development and adoption of internationally-aligned standards in Australia.

Aurelie is an expert in governance, data ethics, privacy and responsible use of technology. She starts by sharing with us how her journey in the data and ethics world started. She is a lawyer by trade, who started as a litigator and then moved on to work in finance for algorithmic trading. Her interest in AI and ethics peaked in 2016, she soon realized all law initiatives regarding AI were done overseas, so she decided to venture into the world of standards and push for Australia to participate in the international standards around AI.

Quotes:

"A use case I see that keeps coming back is 'how do you manage privacy, bias and accountability?"
"The solution you use to address fairness and bias should strongly align with the legal principles of fairness and bias."
"If you don't have the right control in place, or the right filters, then your output can be problematic."

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We’re starting 2021 with Silvio Giorgio, GM Data Science & Strategy at Australia Post. He is an energetic senior leader appointed by the Group CFO to disrupt finance from within. Silvio establishes and leads Data Science for Australia Post applying artificial intelligence, machine learning, predictive modelling, robotics and much more to improve people's safety, customer experiences and commercial outcomes. He was named #1 in the #IAPA Top 25 Analytics Leaders Program 2020.

Quotes:

"We’re not doing dashboards anymore."
"If this is where the future is, then what are you going to do?"
"It popped into my mind the concept of doing magic with data. And it wasn’t about what we would do, it was about what people would see. We needed to make people excited about it, we needed to make people think it was magic and want to see more, to sparkle the curiosity that magicians do."

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In our last episode, we have Alan Ho, Head of Global Partner Marketing at Tibco Software. We look back on the challenges brought by this year and, as it comes to an end, we reflect on what we need to do differently moving forward and what areas offer hope to those who wish to participate more actively in the data economy.

As a part of today’s discussion, we also had the pleasure of chatting with Sara Tiew, Public Sector Leader & Job Redesign Practice Leader at Mercer around the topic of the future of work.

Sara acknowledges the fear people have of losing their jobs due to technology and automation, but she clarifies that technology will also create several million new jobs. Therefore, it’s extremely important to help workers employed in roles that are at risk and transit them into new job opportunities available.

Quotes:

"When I look at diversity I am not just looking at gender diversity, I’m looking at cultural diversity. I’m looking at orientation diversity."
"I want to spend my time giving back to society and what can I do in my capacity to support those that require this help."
"The Pandemic has brought forward digitization. It has impacted such a great deal in countries that are not ready will face a bigger challenge, especially from a skillset perspective."
"If you look at banking and tech sectors, they are always at the forefront of adopting technology."

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We are joined by Disha Goenka Das, Director of Marketing at Twitter APAC to discuss the modern CMO's job.

Disha has had a great career in marketing and has experience in several areas like sales, operations, product management and product marketing. She describes herself as endlessly curious and always wanting to explore the world. Creating marketing strategies and campaigns purely for customer delight is her happy place.

She acknowledges technology is moving at a dramatic pace, and marketing is one of the most technology-dependent business functions, so she advises all marketing professionals to make a disciplined effort in staying up to date with the new technologies coming along.

Quotes:

"If I had to give advice to anyone who is in marketing I would say that marketing and technology are moving at a dramatic pace, it’s moving at a faster pace than any of us ever predicted. Keeping ourselves up to date requires structured learning and working. Whether you are a CMO today or you are a marketing manager you have to make sure that there is a disciplined effort in learning the new tech that is coming as we move along. That is one thing that will be critical for marketing professionals to succeed going forward."
"The other advice I would give for any young marketing professionals starting out in marketing right now is, looking back from my experience, try to spend some time in sales to try to understand the end customer and figuring out what they really care about. Also, on the product side to understand, for example, how engineering makes product decisions. I truly believe it makes you a better marketer. Whether you can do that through rotation programs or spend a year or two in these roles, it is going to be super critical for understanding the business’ core."
"I feel CMOs need to be both analytical and creative. Exceptionally successful CMOs who are at the leading edge of technology, deeply first understand the questions that they’re trying to answer, work with data to figure out how to answer it and most importantly, how do they use that information and data to bring it to life in a way that is truly beneficial. That balance is something important and I don’t see it going away despite how much AI and ML comes into the world."

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Kate Carruthers, Chief Data & Insights Officer at the University of New South Wales joins us for an episode that discusses the results of a survey shared by She Loves Data across their community regarding women's expectations about their careers, given the challenges of the past year.

To start us off, Kate shares how her change-ready attitude has allowed her to seize new opportunities and learn new things. Though it might be intimidating, she encourages others to step up and say ‘yes’ to challenges.

The pandemic of 2020 changed the nature of work forever, affecting millions of careers. Sadly, more women were hit harder. For this reason, She Loves Data reached out to their community and asked professional women about their worries, challenges and aspirations regarding work.

Her advice for women listening is to find a community of people like them and groups that support them. Surrounding yourself with others may provide helpful and valuable perspectives that you might have not yet considered.

Quotes:

"I got my start in IT by being in the kitchen of the National Trust and making a comment about the network to the executive director and she said, you seem like you know what you’re doing, you should be in charge, and I said, sure."
"I say yes a lot. When new opportunities come up I have a tendency to say yes because I’m very change-ready. I’m at the front-end of that change curve, I’m not one of the people at the back end of the change curve. I think that’s so important because it gives you the opportunity to try, and you might fail but you might not. I’ve succeeded more than I’ve failed, on average."
"Getting everybody together because in the modern world it's much more complex and no one person has the solution to the problems. It needs to be a collaborative effort and if you don't bring people together you don’t get the best solutions. You don’t get that diversity of thinking from the different perspectives, and that brings better and richer solutions."
"I was suddenly responsible for all computer systems in the organization and I had no idea of what I was doing, but I got up to speed and I really had an amazing opportunity and I took it."

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We are joined by Celine Le Cotonnec, Chief Data & Innovation Officer at Bank of Singapore and Shameek Kundu, Chief Data Officer at Standard Chartered Bank.

They share with us their perspective on what the function and goal of the Chief Data Officer is. From Shameek’s experience, the function of the Chief Data Officer role varies by industry, maturity of the organization with respect to data and sometimes even by geographic location.

Celine refers to a study that shows a successful data transformation is 20% about tech, 50% about the people’s mindset and 30% process reengineering wherever you are implementing some type of data product. For her, one key aspect of the CDO role is how you manage the people to achieve transformation within the organization.

Quotes:

"Many banks that have built some kind of credit model, even with traditional analytics, have seen their models crumble with Covid-19."
"In any successful data transformation; 20% is about the tech, 50% is about people and their mindset and 30% is about process change."
"If you want to detect financial crime, absolutely the holy grail of AI, in order to detect something like financial crime, at least in supervised learning, you need to know that a transaction was financial crime. But all we can do is detect potential financial crime, then we go and report it to a regulator and we never hear back."

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In this episode we have Figen Ulgen, Head of Data & Analytics at Woolworths NZ and Angela Kim, Head of Analytics & AI at Teachers Health joining us for a conversation around soft skills and their relevance in the world of data.

Figen gives soft skills the same importance as technical skills, given that both of them are necessary for individuals to reach success.

Soft skills may come naturally for some people, but even those who struggle can learn them. It may not come automatically, but it is definitely possible to improve. Angela encourages organizations to provide training for their teams in this area, to help them understand each other and their differences better, and also to learn from each other.

Quotes:

"If you don't have the communication skills or the ability to try and see things from their perspective, I think it is pretty difficult to get to the actual analytics part."
"We need to be able to socially well coordinate relationships because it is adding another dimension to the workplace."

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On our 7th episode we are joined by two very talented women, Siew Choo Soh, Managing Director & Group Head of Consumer Banking and Big Data/AI Technology at DBS Bank and Terisa Roberts, Director and Global Lead: Risk Modelling and Decisioning at SAS.

Siew Choo shares with us DBS’s mindset around innovation. For them, the use of technology does not equate to innovation. They don’t have a designated innovation department, just a small innovation team whose job is not to innovate, it’s rather helping other people across the bank to innovate. They provide resources to enable everyone to be able to innovate by empowering the individuals and teams to be curious.

Terisa draws attention to a recent study by Deloitte, showing that firms who invest in diversity and innovation are 8 times more likely to achieve their business outcomes. She also mentions that in Risk Management, they monitor the algorithms they’re using in society and how these algorithms can discriminate against certain groups. Whenever they are faced with these challenges, a diverse team is in charge of identifying and remediating these biases.

Quotes:

"Their job is not to innovate, their job is to help other people across the bank to innovate."
"You have to introduce bias to unbias your algorithm, because the algorithm has been trained by biased data."
"Because of historical biases that were present in the training data that the algorithms learned from, that was perpetuated. A diverse team would have been able to stop that bias from becoming operational in the way decisions are made within that firm."
"To make them understand each other from the different background they have, whether from cultural perspective or job experience perspective, can sometimes be quite a challenge, but I would say that once you manage to go through that initial phase you’ll see that it will help you get the best results, if you have a very diverse group of people."

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In this episode we have Vidyarth Eluppai Srivatsan, Director of Marketing Technology at The Coca-Cola Company and Pavel Bulowski, Chief Marketing Officer at Meiro and Co-Founder of She Loves Data.

Vidyarth describes the Director of Marketing Technology position as a bridge between the roles of Chief Data Officer, Chief Marketing Officer and Chief Information Officer. It’s about going across the marketing, digital or technology to bridge the knowledge gaps and come up with both the strategy and execution.

Data’s importance in marketing has soared. Currently, there are over 8000 AI and automation platforms available for marketers to take advantage of, so it can be tricky to know which ones are worth our attention and money. Pavel emphasizes the importance of thinking of them as strategic purchases and looking for the ones that offer capabilities that will help differentiate ourselves from the competition.

Quotes:

"Often when I work with people on my team who have that one very linear trajectory in their career, they are good at what they do but they don’t see a bigger picture. That’s what I can bring to the table, synthesize across the board a little bit more."
"This role is a bridge between all those roles, CDO, CMO, CIO, that deal with marketing. There is a definite knowledge gap. We can’t expect all of these functions to know everything about how the fundamental layer that connects each of these buckets to really come through, and that to me is the central role of anyone who heads up the domain of marketing technology. So regardless of where this role sits, this particular person has to think about going across the marketing, digital or technology to bridge the gaps and really come up with both the strategy and the execution of the angles. That in essence is what the role entails, the definition of the structure will vary across companies but the purpose is to bridge the knowledge gap between these departments."
"It’s a bit of a blindside because you can’t drive businesses by data, I think that’s a bit of a misconception. Business has to be driven by business, you need to have business objectives.In my mind it’s business driven and informed by data, but not data-driven on its own. When people put it out, a bit out of context and into the open it often feels like there are a bunch of data scientists who are able to find these whole new insights and directions for a company, and it is not necessarily so straight forward. Often, data analysts don’t have the business context. I think over the next couple of years it’s going to go back and we are going to hear more of business reasoning in marketing again, supported by data, of course."

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With AI determining many system-led decisions, from who gets a job, a loan or business opportunity, the need to ensure diversity and inclusivity in the creation of algorithmic rules becomes more important.

In our fifth episode, we have Glenda Crisp, Chief Data Officer at National Australia Bank. She started her career in tech as a programmer in a bank in Canada and then went on to get an MBA. Before being recruited by NAB, she worked at TD Bank in Toronto. As the Chief Data Officer at National Australia Bank, both her business and technological side come together.

Diversity is a key topic at NAB. They have 6 pillars of inclusion that they target, which are: Gender Balance, NABility and Neurodiversity, NAB Pride, Cultural Inclusion, African Inclusion and Indigenous. When it comes to bias you have to actively look for it and manage it, given that AI learns from data, historical data is a result of human decisions and humans are biased. NAB has an ethical framework for the use of data, machine learning and AI.

Quotes:

"Nothing moves faster than technology [so] pay attention to the big themes that are going on across industries."
"We can’t just ask AI to be more fair but we actually need to prescribe math that is fair."
"The one thing I always say to young women when I get the chance is ‘please do not become complacent.’"

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In this episode, we have Virginia Wheway Vice President of Data & Analytics at Koala and Jan Sheppard, Chief Data Officer at the Tertiary Education Commission in New Zealand.

Diversity is a broad area, and it includes gender, age, ethnicity, etc. Fostering diversity in an organization starts with the recruiting and hiring process.To make sure you hire the right people for the job you need to establish what you need or want for that role before you start recruiting, and then take out all your biases that might come into play.

Diverse teams need leaders who are brave and ready to get outside their comfort zones, stay tuned to learn their tips on how to foster diversity in your team!

Quotes:

"In another company I was at before, the machine learning people have been repurposed to design dashboards."
"The sorts of people I hire are not so rigid that they will only do one thing."
"The real skills that are shining through and that people need are flexibility and adaptability because covid has made us in many ways. We need people who are adaptable and also understand the business."
"Especially when appealing to women, it takes a different description. A lot of job descriptions across the board use male language, very direct and definite language. There are a lot of lessons to take out of this, but certainly how we advertise ourselves and how we build our relationships."

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When lockdowns started, people around the world, especially women, found themselves juggling with their personal and professional lives. In this episode we have Kathryn Gulifa, Chief Data & Analytics Officer at Worksafe Victoria, and Stuti Sharma, Director of Data Science at Visa who share with us the challenges they faced while trying to cope with the new normal.

Stuart Garland, Director at Talent Insights Group gives us the recruiters’ perspective on the covid impact and tells us employees are not the only ones adapting to changes, employers are doing so as well. Some of them are beginning to realise remote work can be productive and perhaps there is an opportunity for some work from home to offer employees flexibility post covid-19.

Quotes:

"I was trying to balance full time work while caring for a 2 year old and a 6 month old. Which I found, honestly, really challenging and had many ups and downs from the mental health perspective, both trying to understand my place in the world having just returned from work and joining back into an organization that was facing a very different economic situation and different priorities than when I left."
"I had my laptop setup on the dining table and it was always there, even over the weekends. I think it was mostly self-inflicted, I would find myself working late in the night and then I realized that it was not sustainable. This thing was not ending anytime soon, and I made a conscious effort to draw a line between my office hours and time with my family."
"I have a team member that says it’s not working from home, it’s living at work."

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Read the full episode summary here: #SheLeads Ep 3

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Anu Madgavkar is a partner at McKinsey Global Institute and she’s done important research on Covid-19’s effect on gender inequality and the future of women at work.

Anu talks about the diversity dividend and the compelling statistics supporting it. A study from +1000 companies around the world, found that organizations in the top quartile in terms of diversity in C-suite executives are 25% more likely to outperform in terms of profitability and long-term value creation. This holds true for gender and ethnic diversity. Also, companies in the bottom quartile are 29% more likely to underperform, resulting in lower profitability or average value creation.

Quotes:

"I started focusing more on this notion that there’s a really leaky pipeline and a lot of women do dropout, and as you start thinking about that and navigating your own journey through that I think I got a lot more interested in why does this matter to the world at large, to economies and to women around the world."
"Looking out 10 years I would say that the pace at which automation is changing the inherent work that we are all doing, that is going to accelerate and that is going to affect all people in the workforce."
"Both automation as well as the trend towards more alternative work arrangements will both likely accelerate post-covid."
"It’s a period of great innovation and experimentation and I think we really need to embrace this time to be more aggressive and experiment with more gender-friendly ways of support, both on the government policies side as well as the business side."

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Read the full episode summary here: #SheLeads Ep 2

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Data Futurology podcast host, Felipe Flores interviews She Loves Data co-founders Jana Marlé-Zizková and Pavel Bulowski, who share how they found a philanthropic niche in data literacy while pushing for more diverse and inclusive workforces. What started as a data workshop for a roomful of friends and friends of friends, has grown into a 21k+ community in 14 (and growing) city chapters around the world. Listen to inspiring stories from them and their members.

Jana and Pavel both have abundant experience in the analytics field and also co-founded Meiro, a Customer Data Platform that allows organizations to harness user data. Jana was recently recognized as part of Singapore’s 100 Women in Tech.

More than just a non-profit, She Loves Data is a community and a movement with the goal of encouraging women across Asia, ANZ, Africa, Europe and the USA to embrace tech and strengthen their data and digital literacy to future-proof their careers.

Quotes:

"There are only like 25% of women in this field. How about going out there and inspiring women to look at data as a possible future career."
"You are changing people’s complete life trajectory by lowering the barriers to entry into something that they can find their passion and interest in."
"It’s never late to change your career, it’s never late to learn about tech and data because it can help you for your future career, your jobs and maybe make your life more meaningful."

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Dr. Alex Antic is the Head of Data Science at the Software Innovation Institute. He has 17+ years of experience across different industries including Federal and State government, Insurance, Asset Management, Banking (Investment and Retail), Consulting and Academia. Alex has recently been recognised as one of the Top 10 Analytics Leaders in Australia by IAPA (Institute of Analytics Professionals of Australia).

He describes the early days of his career as a time for personal growth and honing in his technical skills. Now, his goal is to use data science and artificial intelligence for public good, and as a way to drive impact and change.

Alex is an enthusiast of the experimentation culture; he believes in the fail fast and fail cheap notion and how it is important for organizations to not be scared of failure, given that this can keep them from moving towards innovation.

Quotes:

"Can’t take out the human element when it comes to analytics. Analytics only gets you so far, you have to think about broader applications."
"People need to think about what problem they are solving and if it needs to be solved by a complex method."
"When you are trying to solve a problem, start with the simple solution first, and add complexity as you need to, as sometimes the non sexy elements will add value to the organisation, such as automating an excel file .. You don’t often or always need to go down the complex deep learning algorithm to extract value, as it will make it difficult to explain and difficult to validate, and simplistic is beautiful in many ways."

Read the full episode summary here: Episode #136

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We have Khalifeh Al Jadda, Director of Data Science at Home Depot. Khalifeh has solid knowledge in large scale machine learning and data mining techniques.

He tells us how in retail some businesses are still running manually, without the use of automation tools, so the job of data science leaders is to educate business partners and show them by example and with data the value that data science and artificial intelligence can deliver to their organizations.

Later on, he explains their workflow is managed by product managers and starts with the data scientist taking the responsibility of building the machine learning model, training the model, validating it and even going all the way towards testing it. Then, machine learning engineers take over and scale up the code, they clean it, perform unit testing and do everything needed for it to be ready for production.

Quotes:

"I started my journey in the industry from that point, which was the first internship I got in 2015. That’s the first advice I give to anyone, if you are in graduate school, if you are a student, make sure to pursue an internship before graduation. It’ll make your life much easier after graduation."
"It’s been an interesting journey, a great journey. I hope that everyone actually goes through those challenges in their careers, because it makes you a better and stronger data scientist."
"You cannot build a team with only computer vision people, or a team with just statistical people. You need to bring people from different backgrounds. That’s what my organization includes. It includes people from all backgrounds."

Read the full episode summary here: Episode #135

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In the 6th episode of our “Bitesize Insights for Data Driven Leaders” Series, we have Carlos Rivero, Chief Data Officer at Commonwealth of Virginia.

Carlos started college wanting to become a civil engineer to help with his father’s business, but later on gravitated towards environmental engineering and ecology given that he lived in Miami and was a witness to the environmental impact on their local marine ecology. Thanks to the advice of one of his professors and mentor, he chose science over engineering.

For Carlos, it’s very important to bring people together and have them understand the value of the work they are doing, why they’re doing it and the impact it can have on real world issues.

Quotes:

"Take money out of the equation and simply focus on what brings you fulfillment and happiness."
"It became clear to me that I loved working with data and being able to tell stories from those data assets that I was working with."
"One of the basic things that I really enjoyed about those projects was being of service, because it was not just geographic information science that I was working on, it was really at the service of something larger than that, of something that had potential impact in our social sphere and that to me was extraordinarily appealing and that was a big driver for the impact these projects had on my career."

Read the full episode summary here: Episode #134

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In the 5th episode of our “Bitesize Insights for Data Driven Leaders” Series, we have our first ever live audience episode with Elisa Koch. She is the Head of Data and Analytics at the AFL - Australian Football League.

Elisa spent 10 years working for Avon Cosmetics, mostly in Latin America. That’s where she had her first encounter with marketing analytics and she fell in love with data once she discovered the link it has with customer behavior. Elisa shares her take on data translators and how she considers they are the first people you need to hire. Several organizations are not savvy enough to know how to apply their data and that’s why they need data translators to make their data approachable.

Stay tuned to learn more about Elisa and the work she is doing at the AFL towards merging the sports analytics and marketing data worlds together to better understand their fans and grow their audiences.

Quotes:

"When I figured out that you can use data to try to predict what will happen in the future, that blew my mind."
"Ask questions, be curious, do your research and figure it out."
"A lot of organizations are not savvy enough to know exactly how to apply data and are even scared of it. The beauty of data translators is they make data approachable."

Read the full episode summary here: Episode #133

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In episode 4 of our “Bitesize Insights for Data-Driven Leaders” Series, Jeanne Holm is the Chief Data Officer at her hometown, the City of Los Angeles. She works at the cross-section of civic innovation, open data, and education, addressing issues ranging from homelessness to digital equity technology innovation, data and analytics, and public-private partnerships.

As CDO, Jeanne focuses on taking data, like the number of people trained in the city’s workforce centers or how sustainable their practices are, and making it available and accessible to the taxpayers. To enable this, she works with individuals within the government that are not data scientists, and provides support and data literacy training in order to facilitate their understanding on how to structure, manage and put context around data. She also cooperates with entities outside the government, like academic researchers, businesses, and advocacy groups who want to use the data but need the information and context around it.

Quotes:

"The thing that connects all of these different pieces is wanting to tell stories that help people create different actions."
"A Chief Data Officer focuses on organising and helping to bring out the data from different parts of the organisation and then either to make it easier to share within the organisation or to also share that out with the public."
"If you set the directions, set the pace, and support people and are compassionate to their own issues; I think you can build and lead an amazing group of people."

Read the full episode summary here: Episode #132

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Steve Monaghan is the Chief Digital Officer at Riyad Bank. He has a series of investments in banking and insurance using AI and new technologies in order to improve efficiency.

He started out as a commercial pilot working 22 hours a day, running the aviation company during the day and flying at night. His first contact with data came when he started using spreadsheets to automate tasks and accelerate the time in which he got them done. His career then moved from flying into technology.

For Steve, the three core laws of technology that drive data science and AI are:

Moore's Law: processing power doubles every 18 months for the same cost.
Metcalfe's Law: the value of a network grows by the square of the network's size.
Kryder's Law: storage doubles every 13 months for the same cost.

He believes these 3 laws encompass the ability you have to assimilate learning into knowledge.

Quotes:

“What technology does is, it arbitrages time, and you get to live in someone else’s future, which is the most powerful proposition on the planet, and especially what data science enables one to do, is to achieve this at scale.”
“One of the main problems in data science and analytics is the adoption of the work, the last mile and being able to have the impact that technology promised.”
“It is all logical, and it is all there , so work it out. Technology and data science is logical, it's only about putting our mindset and attitude towards it and working it out. Because everything we do in the tech space is driven by some level of logic, and if its logical it can be done. Don’t accept your own limitation find a way to work around it.”

Read the full episode summary here: Episode #131

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Christopher is the Global Head of Health Economics & Outcomes Research at Abbvie, an adjunct professor at NYU Graduate School of Public Service and active board member of several influential organizations.

He has received numerous recognitions in the past. In 2019 he was named a Top 100 Innovator in Data Analytics, in 2018 an Emerging Pharma Leader and in 2017 he was a Top 40 Under 40 in Minority Health Honoree by the National Minority Quality Forum

Christopher explains how, in his eyes, all human data is healthcare data, and how excited he is at the applications this data might have if we learned how to analyze it in a better way, given that, currently, only 5% of all the available data is being analyzed.

Stay tuned to know how the broad array of experiences he had in organizations like non profits, government and pharmaceuticals helped him learn to think outside the box and gain a broader perspective.

Quotes:

"I'm just not feeling it, it's just not meaningful to me and I just want to help people.
"In my eyes, all data is healthcare data."
"I think that the problem that we have is we're still in a society where there's fear of the unknown and I also think that in some cases the system is set up to benefit folks from having a level of fragmentation even lack of data quality."
"You also have to identify what is the benefit or value proposition for each of these individuals or groups within the organisation for them to really buy into it."

Read the full episode summary here: Episode #130

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Steve is an investor, a partner at TEN13, and you may also know him as one of the sharks from the show Shark Tank in Australia. His journey in entrepreneurship and investments began over 25 years ago when he started his first company in the telecommunications area. He sold his second business in 2010 and went on to become an investor. Currently, he has an existing portfolio of over 30 companies.

He was Queensland Chief Entrepreneur for three years, he does mentorships and is also a qualified pilot. Steve has several investments in the US and he draws a comparison between the start-up scene there and in Australia.

Stay tuned as Steve shares his life experience and provides both tips and inspiration for anyone thinking of starting their own business or already working in their start-up.

Quotes:

“Really bad investments are easy to find… if you can sell to your customers it is a far easier way forward.”
“If you're doing it and you know it's wrong, stop it and do anything else.”
“The thing about bad news is when you hide it, it just doesn't get any better.”
“You're either shooting the lights out or shooting your brains out”

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Read the full episode summary here: Episode #129

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Felipe Flores es director de data science con casi 20 años de experiencia. Felipe ha trabajado en ingeniería de datos / almacenamiento, reportes, inteligencia empresarial, análisis, data science, machine learning e inteligencia artificial. Actualmente es el Director de Data Science en la empresa de Inteligencia Artificial, Honeysuckle Health.

Mientras que Day es una experta en data science. Durante 15 años, ha colaborado con corporativos internacionales como Colgate, Gillette, P&G, Walmart y Epworth HealthCare para guiar su estrategia de inteligencia empresarial, de mercadotecnia y decisiones operativas mediante el uso de datos.

Le apasiona todo lo que tenga que ver con datos e insights, es experta en técnicas cuantitativas, cualitativas y analíticas y ha trabajado con bases de datos, inteligencia de mercados, reportes de datos, ciencia de datos, pronóstico de ventas para innovaciones, machine learning e inteligencia artificial.

En este episodio Felipe y Day nos dan una breve pero integral introducción a que es Data Science. Platican de cómo desarrollar las capacidades de data science en una empresa y la importancia de la cultura para implementar proyectos de datos exitosos. También nos dan tips de como empezar a desarrollar tu Carrera en Data Science entre otros temas fundamentales de Data Science.

Temas:

[03:20] ¿Qué es Data Futurology y ¿cuál es su objetivo?

[07:30] Trayectoria de Felipe Flores

[16:30] ¿Qué es Data Science/Ciencia de Datos?

[19:45] Tips para desarrollar las capacidades de Data Science en una empresa

[24:10] Sugerencias para alguien que empieza su carrera en data science

[27:20] Como mantenerse al día en el mundo de data science dado el avance de la tecnología

[32:30] Cómo especializarse en data science si mi carrera no está relacionada en data science

[34:10] Entendiendo las diferencias de inteligencia artificial, machine learning, deep learning y data science

[43:10] Cómo interactúa la Mercadotecnia con Data Science

[44:35] El rol de la cultura en una organización para implementar data science

[48:00] Tipos de algoritmos

[53:50] ¿Qué es la minería de datos?

[55:30] Libros recomendados para Data Science

Frases:

“La idea de un científico de datos es poder mejorar el desempeño de una empresa ocupando datos”.

“Data science no es una caja de pandora sino hay una explicación detrás de cada algoritmo”.

“El error más común es que la gente espera terminar el proyecto y decirle a un área que le va hacer el trabajo mucho mejor; la gente que tiene que generar el cambio deben colaborar con la gente de data science desde el principio de un proyecto”.

“Nadie puede mantenerse al tanto de todo lo que está pasando en data science; es importante entender lo fundamentales de machine learning y estadística y con eso cualquier desarrollo que se de en cualquier área y así cuando necesitas aprende en cualquier área”.

“En general el conocimiento depende de la persona cuando uno se quiere exigir y esforzarse; el conocimiento existe lo más importante es practicar poner los conocimientos en práctica”.

“Uno puede tener los planes más espectaculares, pero si la cultura no existe nada va a funcionar; la cultura es el ingrediente más importante de data science”.

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Elliot Smith is the CEO and founder of Maxwell Plus. A visionary company that has been using machine learning and artificial intelligence to help treat and diagnose patients with prostate cancer. The way they bring in the data, help analyze it and help people along their journey is fascinating and makes them leaders in this specific field.

Elliot is very passionate about helping others find diseases at a point where they can be treated. To know where things are, if they should be treated and how best to treat them is his ultimate goal in terms of applying data science and AI to the medical world.

Stay tuned to know how his desire to build something that could be useful in the real world turned into a company with the potential of impacting the lives of over 300 million men that have or are being diagnosed with prostate cancer.

Quotes:

When I finished that I sort of came to the realisation that I loved academia, but it was a little bit too slow moving for me. I wanted to be out there building things, putting stuff out into the world and decided to have another run at building a start-up and at the time I happened to know a lot about numerical modelling, optimisation and how that translated into things like AI and also medical imaging and without jumping too far ahead that's the point where all of this came together and the company that I now run Maxwell plus was born in that collision of worlds.
We looked at their data, we saw something in their data that wouldn't have been picked up otherwise and our doctors said we need to act and with the hindsight of knowing their outcomes we acted at the right time and those people have what is for prostate cancer is 98% 5 year survival rate because we acted in time to avoid that conversation.
If you have had treatment or you choose to go into what is called active surveillance, which is let’s just watch this and determine when to treat, the algorithm that we apply to do initial diagnosis apply just as well to monitoring your diagnosis over time. How can we then go to provide ongoing support to really see these men through from day one to day infinity and continue to provide real clinical value to these men.

Read the full episode summary here: Episode #127

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In episode 2 of our “Bitesize Insights for Data Driven Leaders” Series, Adam Bonnifield is a VP of Artificial Intelligence at Airbus. He is a former Chief Product Officer (Census) at the U.S. Department of Commerce and Co-Founder of Spinnakr and Giv.to. Adam lives and breathes online engagement - he ran digital strategy for political campaigns since he was in college, breaking Congressional fundraising records. After graduating from Cambridge University, he was awarded the Tsuzuki Fellowship to direct online youth outreach in Japan.

Enjoy Adam Bonnifield in our Bitesize Insights for Data Driven Leaders” Series!

Quotes:

“Don’t talk so much when you’re the boss. Just stop talking so much about stuff.”
“You have a sort of natural optimism because you have the experience of building something from nothing. And realizing that anything can be done, especially in data technology. At the same time, you’re a child when it comes to navigating corporate politics.
“I was the kid that went to the Aerospace Museum every week. I am in love with aircraft. When you work with a company with a specialized product, you’re surrounded by people with a patriotic feeling for what they do.”
“It was a special time because it wasn't really known what you could do with data.”
“If you're working in data science, I would look for companies with complex supply problems."
“We need a new paradigm of data storytelling and data visualisation built for regular people.”

Read the full episode summary here: Episode #126

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David is the Senior Advisor for Data and Artificial Intelligence at UnionBank Philippines. Concurrently David is an external advisor to Singapore's Corrupt Investigation Practices Bureau (CPIB) in the capacity of Senior Advisor (Artificial Intelligence) and to Singapore's Central Provident Fund Board (CPF) in the capacity of Senior Advisor (Data Science).

David loves the idea of programming logic. We have the ability to use information as mechanisms to assist us on a day-to-day basis. So, David was inspired to explore the possibilities of data. After high school, David considered artificial intelligence or astrophysics. However, he ended up finding a place that has a combined degree of computer science and artificial intelligence. In fact, he was the second student to complete the degree.

Stay tuned as David gives his perspective on data governance – it's a mechanism that allows us to identify what we should and shouldn't do. In addition, David reveals his views on AI governance, and David explains how he balances all of his work.

Quotes:

“I was called a geek many, many times in my life, so I’m going to own it now.”
“The objective is: how do you take these very complex ideas, and explain them in very pragmatic, very relatable terms.”
“I personally have a bit of an aversion to the term [data scientist], for two reasons: every scientist uses data, and it’s just too generic.”
“We need to also equally realise when data isn’t relevant, … a process is what’s required.”

Read the full episode summary here: Episode #125

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Dan Costanza, Chief Data Scientist at Citi, joins me for the first episode in the new “Bitesize Insights for Data Driven Leaders” Series. Dan opens the show by explaining how he got interested in data. After graduating from college, Dan went into an investment banking role. Eventually, he received an exciting project that got him started down the data path. Dan says as someone who didn’t study computer science in school, it has been a heavy lift trying to get those technical skills up to par. During a code-heavy project, Dan needed to learn how to break up the project and work through it. Also, he learned how to think about sampling data without bias.

Then, Dan explains the importance of emotional intelligence for data science. Conscientiousness and emotional intelligence are the things that you can actually interview for. Instead of judging people on their grades, we need to judge people on their ethics, communication skills, and willingness to work in teams. In India, Dan set up a data science team. The talent in India is insane. However, there are cultural differences Dan needed to work through. For instance, he told his team that they needed to speak up when they had ideas. If you create space for people to bring their own thoughts, you’ll hear loads of good suggestions. Before Dan told his team that, they would withhold useful information.

Quotes:

“When you look at the hiring research again, like there are two real categories and the one is things you don’t interview for, which are the intellectual horsepower things and those are - how smart you are, do you have some specific skills I need. The word that always comes up on the other side is conscientiousness, and that encompasses the stuff we talked about at the beginning, and the emotional intelligence, teamwork parts of it and those are the things you do actually interview for. Which is counterintuitive for a lot of people who work in quanti type roles because you want to ask people really hard questions, to see if they are smart, but the problem is the data doesn’t support that as being predictive of anything when you control for their grades.”
“You start by spraying things around, working with a lot of people, just to get the volume in and see who those people are and meet people, and as you work a little bit, you start to understand their own types of workflow.”
“More powerful then compliant is having good ethics there on the ground.”

Read the full episode summary here: Episode #124

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Michelle is Head of Enterprise Data Governance at ANZ Bank. In this role, Michelle is responsible for Data Governance strategy across the group, ANZ Data Privacy and Ethics framework, Enterprise Metadata Management, Data Risk and a Centre of Excellence for Data Quality. Michelle has over 25 years of experience in the world of Data and utilizes that knowledge to help foster organized and sustainable large scale analytical environments for commercial success. Michelle has a particular interest in building frameworks for ethical data use by successfully developing ANZ’s principles for Data Ethics.

In this episode, Michelle describes the importance of transparency in data ethics. For instance, in the world of insurance, people want to know how their premium is calculated. What information is the company using to calculate the insurance premiums? So, the insurance company needs to explain what information they are putting into the black box. Insurance algorithms for premiums are like the formula for Coca-Cola; no one ever shares it. To combat this, Michelle needed to put herself in the mindset of the customer.

Stay tuned as Michelle explains how the customer journey can benefit from data governance. Also, Michelle reveals where she thinks the future of data ethics is going, how data ethics overlaps with data governance, and the ways that consent fits into data ethics.

Quotes:

“I did a lot of work in redesigning documents and determining what information to disclose and that really comes down to transparency and transparency is a big aspect of data ethics.”
“That was a legislation where you actually had to articulate what are the factors, what is the information that you give us that we actually then use to calculate your premium. So it took it from being an enigma to explaining these are the inputs that we put in. And that's got a lot of relevance to today's world with artificial intelligence which is what information are you putting into that black box to then produce the outcomes that are going to impact me. And so I guess that's the genesis of it and that was a really big change in the insurance world because insurance algorithms for premiums are like the formula for Coca-Cola nobody ever shares them anywhere.”
“I'm not shy about saying you know data is not part of the IT world, it's part of the business world.”

Read the full episode summary here: Episode #123

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DialogCONNECTION was founded in 2008 by our Director and Lead Consultant, Dr. Maria Aretoulaki. Maria has been designing Speech IVRs and Voice User Interfaces (VUIs) since 1996, long before Voice Assistants, but also before telephone self-service was mainstream. She got into Voice through a Post-Doc in Spoken Dialogue Management for Speech recognition applications, after having got a PhD and an MSc in NLP and Machine Learning and a BA (Hons) in Linguistics & English.

In this episode, Dr. Maria explains how she started in the data space. When looking through the list of available master's degrees, she found one in machine translation and was utterly fascinated. Dr. Maria learned how to translate Spanish sentences into English sentences using machine translation. There are all sorts of rules and contexts that the machine needs to learn. For instance, because language is complex, it's difficult for a machine to pick out which sentences belong in the summary of an article. Humans can't even all agree on which sentences belong in a summary!

Quotes:

“[To start your own company] You're never that prepared.”
“Usually we were approaching people to do a project, but rarely would "go live" materialise.”
“Flexibility, multitasking and resilience are the main lessons.”
“How you formulate a question is really important.”
“But also you get an insight into things you haven't even thought about.”
“Every single connection is valuable”

Read the full episode summary here: Episode #122

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Craig Rowlands is the Senior Executive Information Management at Medibank. Craig’s role is to provide thought leadership, aligned with expertise in delivering pragmatic business focus information management solutions whilst leading improvements in the data landscape. Whilst at Medibank, Craig has transitioned Medibank from a legacy data management team to a cloud first data capability based on a DevOps methodology to provide Medibank with the right technology, process and people to achieve its long term aspirational objectives.

He has an extensive background in data, commencing in the UK until he moved to Australia in 2011. Whilst in the UK Craig worked for blue chip banking and finance industry leaders such as Barclays, First Direct, GE and Hbos. His first role in Australia was with ANZ as Head of Decision Systems. Craig joined Medibank from Latitude Financial Services (formally GE) where as Head of Data Management he led a large team responsible for data strategy & design, data governance and business intelligence reporting.

Quotes:

"You reach a crossroad in your career as an analysts where you either stay true to yourself and be a technician or you move into a leadership position role where you bring others along the journey."
"There is so much variety in data today. From the data science world to the reporting world to the information management world and all the roles in there. To become a good leader, my view was I needed to know at least a little bit about each area."
"[Helping the customer] is the outcome and the power that data can provide."
"So, my philosophy has always been I give them 2 or 3 small wins backed up by 1 big one. We keep the momentum going because that way we can keep the seed funding going."

Read the full episode summary here: Episode #121

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Catherine Havasi's dream and mission are to give computers the ability to think about the world more like a person and less like a machine. Equally important is putting this innovation into practice in a way that effects real business outcomes and transforms our everyday lives. Catherine has twenty years of experience in directing and doing cutting edge research, operationalizing it, and using it to drive real results.

In this episode, Catherine explains how she started in the data world.

Read this full episode summary on our website:#120 Natural Language Processing with Catherine Havasi – CEO

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Dr. AnnMaria has a proven track record in founding successful businesses. 7 Generation Games is her fourth company and a spin-off of her consulting firm, The Julia Group. She was previously the president of R&R Consulting and Vice President of Spirit Lake Consulting, Inc. and is also currently CEO of the Julia Group. Both the Julia Group and Spirit Lake Consulting went on to generate over $1 million in contracts. She has authored grants that have received tens of millions of dollars in funding, including more than $30 million for Native American programs.

Read this full episode summary on our website: #119 Finding Success in Data-Driven Entrepreneurship with Dr. AnnMaria De Mars – President

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1 in our new Innovation and Entrepreneurship Series, Andrew is an Ophthalmologist (Eye Surgeon) and Associate Professor in International Eye Health at the London School of Hygiene & Tropical Medicine. He has worked and researched in over twenty countries, including two years living in Kenya, where he led a significant eye disease study and the development and testing of Peek.

In this episode, Andrew reveals his own story and how he found out about his poor vision. When Andrew got glasses, his life completely changed.

Read this full episode summary on our website: Smart Tech Meets a Smart Dr’s Passion to Solve Blindness with Andrew Bastawrous – Co-Founder & CEO

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Max Métral is a self-motivated professional with a successful track record in data science and analytics cross-functional roles for worldwide organizations. In this episode, Max dives deep into his background and how he found his interest in data.

Read this full episode summary on our website: #117 Driving commercial opportunities with Max Métral – Senior Analytics Manager

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In this episode, we hear a panel discussion from Stuart Garland- Director of Blink Recruitment and Data Futurology Director & Podcast Host, Felipe Flores. With the current pandemic drastically affecting the Australian Data Science market, it can feel scary and unknown to people who are looking for a new role or career change within the industry.

Read this full episode summary on our website: Navigating The COVID-19 Landscape: Changes For Data Scientists in the Job Market

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At Queen's University, Stephanie Kelley is a Ph.D. Candidate in Management Analytics at the Smith School of Business. Her research focuses on the ethics of analytics and artificial intelligence in financial services. She uses methods from management analytics and organisational behaviour to understand the causes and prevention methods for AI ethics challenges.

Stay tuned as Stephanie discusses measuring ethics, how countries are taking a stance on AI ethics, and why AI ethics are a competitive advantage.

Read this full episode summary on our website: AI Ethics with Stephanie Kelley – PhD Candidate in AI Ethics at Queen's University, IEEE Ethically Aligned Design for Finance Working Committee Member

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Rishal is a solutions architect at Entelect. Rishal founded Prolific Idea in 2015, where innovation is cultivated through research and technology. Prolific Idea has since launched a collaborative productivity platform, Hivemind, and is currently building a straight-through document processing platform, Viszen.tech.

In this episode, Rishal dives deep into his story and explains how he got involved in the tech industry. Read this full episode summary on our website: #114 Grokking Artificial Intelligence Algorithms with Rishal Hurbans – Solutions Architect

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Dane Hillard is a software engineer and web developer interested in education, biotechnology, and open source. Today, he shares about the exciting world of web development and how he started. Dane speaks about the future of web development.

Read this full episode summary on our website:

113 Practices of the Python Pro with Dane Hillard – Author and Lead Web Application Developer

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Have you ever gone to a meeting to show the outputs of your project, and it goes terribly wrong? When a meeting goes sour, your project gets delayed, or you have to completely restart. If you are meeting with all your stakeholders at once, that is not the real meeting. Instead, Felipe says to have one-on-one sessions with each of the stakeholders well before the group meeting. This will take more time, but everyone will get dedicated attention. If you do this well before the group meeting, then you will have time to adjust your work. Overall, this will help your stakeholders be more bonded to your project and eventually turn the odds in your favor.

Another way to ensure success in your work is by having regular working sessions with your stakeholders. They are working sessions, so you need to be vulnerable and show what you are up to. Make sure the stakeholders know that their input and concerns have gone into your work. This will help develop strong relationships with your stakeholders, and your work will have more impact. Plus, if and when something goes wrong, your stakeholders will be on your side and will likely help you solve it.

Enjoy the show!

We speak about:

[01:30] Why you should have one-on-one meetings with your stakeholders
[02:50] Create ongoing and regular update meetings with your stakeholders
[05:40] How your stakeholders will help you solve problems

Quotes:

“Take your stakeholder’s feedback and improve the project.”
“Give each stakeholder dedicated time.”
“The group meeting is not the real meeting.”

Thank you to our sponsor:

Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Chris Mattmann is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he’s faced at NASA, including building an implementation of Google’s Show & Tell algorithm for image captioning using TensorFlow. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval.

Enjoy the show!

We speak about:

[00:30] About Chris Mattmann
[02:30] How Chris started in the data space
[08:15] The transition to management
[10:00] How does IT navigate different life cycles of people?
[12:35] What’s an example of a bottom-up project?
[15:10] How have you seen the importance of machine learning rise?
[17:20] Do you have large amounts of data?
[21:25] What are the hardware challenges of space?
[24:45] About Machine Learning with TensorFlow, Second Edition
[30:00] About the Hidden Markov model
[34:10] What kept you going through the first edition?
[35:40] Why is TensorFlow your favorite framework?
[39:00] How do you find time to write?
[39:30] What other activities do you do for the community?
[41:20] What are you working on at the moment?

Resources:

Chris’s LinkedIn: https://www.linkedin.com/in/chrismattmann/

NASA Jet Propulsion Laboratory on LinkedIn: https://www.linkedin.com/company/jet-propulsion-laboratory/

Chris’s Twitter: https://twitter.com/chrismattmann

Machine Learning with TensorFlow, Second Edition (For 40% off use code poddatafuturology19).

Check out other books from Manning Publications, use code poddatafuturology19 for a 40% discount.

Quotes:

“TensorFlow is everything you need to know about machine learning.”

“The thing that kept me going was the realization that AI was the future.”

“Python is the data science framework nowadays.”

“I see the future.”

Thank you to our sponsors:

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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During this special episode, Felipe gives a presentation on explainable AI methods for structured data. First, Felipe talks about opening the black box. Algorithms can be both sexist and racist, even at massive companies like Google and Amazon. Removing bias in AI is a difficult problem. However, there are ways to overcome it. Where does the bias come from? The dirty secret is that the data is biased. The algorithm doesn’t decide to be biased, it learns to be biased from the data. In reality, AI puts a mirror on society. We have inherent sexism and racism in our society. AI is a tool that will help us eradicate these underlying issues in society. No one should be attacking the people that made the algorithms.

The data is a representation of the world. We use the explainable methods to interpret what is happening in the algorithms. Explainable methods include explainable algorithms and unexplainable algorithms. When we come across an unexplainable algorithm, we can hit them with a framework and try to make them more explainable. Then, Felipe explains decision trees using the Titanic. Start with a list of all the people who boarded the ship, then separate them by gender. Next, you can use your clear rules to find which passengers survived. The model will give you a good summary of all the data depending on the rules.

Felipe would come across people who said predictable algorithms need to be 99% accurate, or they are garbage. However, if you are predicting how a person will behave, the accuracy will be lower because no one can predict how someone will act. Then, Felipe explains LIME: Local Interpretable Model-Agnostic Explanations. Regardless of the approach, you can use LIME to understand the predictions of an individual person. Stay tuned as Felipe explains the random forest.

Enjoy the show!

We speak about:

[02:10] About Felipe

[04:00] Opening the black box

[07:20] Where does the bias come from?

[11:20] Making more transparent algorithms

[17:00] About decision trees

[19:45] Using interpretable models

[22:20] About LIME: Local Interpretable Model-Agnostic Explanations

[30:10] How to use a random forest

Resources:

70 Making Black Box Models Explainable With Christoph Molnar – Interpretable Machine Learning Researcher

Quotes:

“The data represents the way that the world works.”

“With the rise of AI, we can choose how we want the world to be.”

“Sometimes, we have algorithms that are just 52% accurate.”

Thank you to our sponsors:

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We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Ken is a senior-level programmer with over 23 years of experience developing software in industry and research environments. Ken is a self-taught computer programmer, software engineer, hacker and teacher. What started off as developing websites in the early 90’s turned into an extensive and fruitful career in bioinformatics and eventually teaching and mentorship. Ken is also the author of Tiny Python Projects published by Manning Publications.

Enjoy the show!

We speak about:

[00:30] About Ken Youens Clark
[02:00] Ken’s Work and COVID-19
[03:15] How did you get into the bioinformatics field?
[13:00] Ken’s first job in bioinformatics
[25:20] How did the design of your book come about?
[35:30] How did you keep going despite your frustration with the technology available in early computer programming?
[40:35] What are your thoughts on hard coding and data processing?
[46:00] What will your next book be about?

Resources:

Ken’s LinkedIn: https://www.linkedin.com/in/kycl4rk/

Tiny Python Projects (For 40% off use codepoddatafuturology19).

Check out other books from Manning Publications, use code poddatafuturology19 for a 40% discount.

Quotes:

“[After COVID-19], the first thing I had to do was become a YouTuber.”
“I’m hoping that anyone who is stuck at home right now can watch my lectures, go through the materials, and teach themselves python at home for free.”
“That’s all computing is, transforming one thing into another.”
“How did you get into that field?”, “A completely random series of mistakes and not knowing what I’m doing”
“Anything that [my boss] asked me to do he could have done in half the time and twice as good, but he had the patience to wait for me to catch up”
“I fell in love with programming - I was like finally I’ve figured out something that’s interesting, that I truly enjoy doing”
“What I see over and over again, especially in science, especially with novice programmers is that everything about the program is hard-coded“
“I haven’t touched a windows computer … since 1999”
“[in science/academia] no one teaches programming … you just generally kinda figure it out along the way”
“I think people should be taught the basics of command lines; piping, redirecting, those basic kinds of things"

Thank you to our sponsors:

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We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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If you are trying to choose what skills you should build as part of your career, no single answer fits everyone. It depends on your strengths and ambitions. Your strengths are the things you are naturally good at. If you're not sure what your strengths are, then think about the things that people come and ask you about. When someone asks you how you do something, most likely that is one of your strengths.

Also, think about where you want to end up in your career. For instance, if you're going to become a manager, find a way to be a leader on a project and develop your soft influence. Hard influence is something that comes from authority. Whereas soft influence is something that we can build upon. Find a way to lead a project among your peers. Taking the lead will allow you to practice your influence, leadership, and management skills. A group of people will follow the person with an organized and well-thought-out plan; be that person!

Another tip - do not be the person that blindly follows what they enjoy; it's not a strategic way to go about crafting your career. Felipe has seen people have a successful career doing work that they enjoy. However, they will eventually lose their love for it. Plus, if you only follow what you enjoy, then you run the risk of getting pigeonholed in an area that may not have future growth. Also, it might be an area that loses demand and importance; it can make your skills irrelevant.

At the end of the day, we need generalists. Companies need people who are knowledgeable in the end to end process. As an industry, data scientists are in high demand. Plus, they need people who have a mix of skills. There are always more roles being added because the industry is starting to understand that a great deal of knowledge is required to build a successful data science team.

Enjoy the show!

We speak about:

[01:15] Think about where you want your career to go
[03:40] Do not follow what you enjoy
[06:25] Why we need generalists

Quotes:

“Think broadly about what will be needed in the future.”
“Overall, the wider you can cast your net when it comes to interests, the better.”
“I recommend that you become a generalist if you want to move up.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organization-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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The Local Maximum is hosted and produced by Max Sklar. Max is a software engineer and new product developer by trade, with a focus on machine learning, Bayesian inference, content discovery, and prototyping. The bulk of his work as a machine learning engineer was at Foursquare, where he built Foursquare City Guide’s critically acclaimed 10-point venue rating system and the Marsbot app. More recently, he led the development of a causality model for Foursquare’s Ad Attribution product and now works at Foursquare’s innovation lab.

Enjoy the show!

We speak about:

[00:30] About Max Sklar
[02:00] How did you get into the world of data?
[06:05] About the recommender system at Foursquare
[10:15] Why do you like solving open-ended problems?
[12:20] How has your new role been?
[14:20] What are the benefits of staying at a company for a long period of time?
[16:30] About Marsbot
[20:00] About the attribution side of Foursquare
[26:40] How do you pick the innovations you are going to try?
[28:50] How does the monthly pitch day work?
[31:20] About Local Max Radio

Resources:

Podcast: https://www.localmaxradio.com/

LinkedIn: https://www.linkedin.com/in/max-sklar-b638464/

Facebook: https://www.facebook.com/localmaxradio/

Twitter: https://twitter.com/maxsklar

Marsbot: https://marsbotapp.com

Foursquare: https://foursquare.com

Quotes:

“Try a few ways and see what works.”
“I am looking forward to seeing what kind of trouble people cause.”
“I focus on making a splash on pushing forward technology.”
“Getting things to work that no one has necessarily tried before - that no one in our company has tried before.”
“It's tough to describe probability functions to end clients, it's always good to have that on your side”.

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organization-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Today, Felipe has a few guests at the Data Science Melbourne Meet-Up Group. Former guest Prashant Natarajan and Romina Sharifpour share about their work with conversational AI. Later, Nic Ryan shares his story and tips for getting started in data science.

Prashant starts by giving some background on the development and basics of NLP. We have made progress with deep learning and bringing machines closer to understanding human language, but it isn’t easy. Humans have a hard enough time understanding each other. What we are asking machines to do is analyze tone, find nouns, and recognize different languages. We already have things like voice to text and smart speakers, but NLP's ultimate goal is for a bot to have a conversation. True NLP is a combination of natural language understanding and language generation.

Enjoy the show!

We speak about:

[00:10] Introduction & welcome guests
[06:45] Basics of NLP & customer service
[17:45] Cleaning data and data processing
[27:30] Key takeaways from an NLP conventional AI project
[42:20] Q&A with Romina and Prashant
[51:50] Meet Nic Ryan
[53:50] Nic’s start in data science
[75:00] Nic’s tips and lessons learned
[80:00] The future of machine learning

Resources:

Data Science Melbourne Meet-Up Group

Prashant Natarajan on LinkedIn

Romina Sharifpour on LinkedIn

Nic Ryan on LinkedIn

Check out books from Manning Publications, use code poddatafuturology19 for a 40% discount.

Quotes:

“When you ask computers to do NLP you are asking them to do something that is the holy grail of machine learning.”
“If we don’t make the human's job easier by using NLP then we have to ask ourselves, what the heck are we doing?”
“You have to think about what your audience wants, not just what you are interested in”
“Consulting is like dating, you meet a lot of people, and there’s a lot of rejection.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organization-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Andrea draws on the wealth of experience gained from her long-standing commitment in the industry, specifically in the areas of Business Intelligence, Data Mining, and Data Warehousing. She studied at the Universities of Dortmund and Sheffield and graduated with a master’s degree in statistics (University Dortmund). In 1999 Andrea branched out to form her own consulting firm rendering personalized services (Business Intelligence, DBM, CRM, and Marketing Services).In April 2012, she took on the role of director strategic analytics, Draftfcb München GmbH, part of IPG Group, now HackerAgency. Andrea’s profound know-how meets the needs of national and international key players in various markets and makes her a frequent lecturer at several universities as well as a well-known speaker at professional conferences. In addition, she is a noted author of two books about data and business.

Enjoy the show!

We speak about:

[00:10] About Andrea Ahlemeyer-Stubbe
[02:25] How did you get started in the world of data?
[04:00] How has the rise of data and statistics impacted your career?
[08:45] Is marketing and analytics something you fell into? Or did you choose to pursue it intentionally?
[12:10] What holds people back from trusting data?
[15:00] Case Study
[21:30] What led you to make the jump to business owner?
[26:40] Tell us about your work in China, Japan, and the US
[29:45] What surprised you when you started your own business?
[32:00] Tell us about your books
[40:50] What led you to write your books?
[42:30] What advice do you have for listeners in the data analytics space?

Resources:

Andreas’s LinkedIn: https://www.linkedin.com/in/andreaahlemeyerstubbe/

Monetizing Data: https://www.amazon.com/Monetising-Data-Uplift-Your-Business/dp/1119125138

Practical Guide to Data Mining: https://www.amazon.com/Practical-Guide-Mining-Business-Industry/dp/1119977134

Quotes:

“That data you use for prediction should be similar to the data situation where it will be used.”
“If people are involved, and you need a decision, it's never done in 5 minutes.”
“It’s not just being an expert; it’s clear you have to be that, but being seen as an expert”
“Sell your work; they aren’t really interested in the beautiful nice methods.”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organization-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

A Practical Guide to Data Mining for Business and Industry

Monetizing Data: How to Uplift Your Business

In this episode, Felipe speaks about what is going on with artificial intelligence in the retail space. Visual search and recommendations are currently trending in retail. Companies are offering the ability for consumers to search based off of a picture. For instance, consumers can take a picture of a pair of shoes and find that product online, similar products, and products that would go well with it. Humans can process images way faster than text. Plus, pictures carry more information than sound. Visual search and recommendations are hot and on the rise right now.

Free shipping and free returns are hurting companies. People are buying multiple items with free shipping and then returning it. As a result, the retailer is paying for transport on both ends. Retailers are struggling with this model. However, free shipping is something that consumers are looking for. So, brands are offering a subscription model or a rental model. Sometimes, a combined subscription and rental model. More and more companies are offering clothing rentals.

Next, Felipe explains what to expect in retail. Automated stores are popping up in the United States from Amazon. You pick what you want in the store; then, you walk out without having to see a cashier. With advancements in AI, the movements of customers can be tracked in retail stores. Voice assistance is also something you can expect to see in the future of retail, like ordering something using an Alexa. Stay tuned as Felipe speaks about the basics of AI in retail.

Enjoy the show!

We speak about:

[00:50] What’s hot in retail right now
[04:35] The future of retail
[07:30] The basics of AI in retail

Quotes:

Humans can process images way faster than text and they carry so much more information than sound. We can process images in about 13 milliseconds and it’s something that is 60,000 times faster than the equivalent in other mediums. So visual search and recommendations as a result of that search - that is hot right now and on the rise!
Once you get past 20% market share or penetration then you start hitting the mainstream and that’s when the product has fast adoption.
What we find is that today’s convenience is tomorrow’s friction.

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organization-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Alberto is an analytics executive with hands-on experience in big data analytics strategy, business development, sales, strategic partnerships, IoT, AI, predictive modeling, product design and development, and solution delivery by interacting with executives at the CXO level, acting as a trusted advisor to Fortune 500 companies, and leading data scientists and IT teams.

In this episode, Alberto dives deep into his background and explains how he started in the data world. It’s Alberto’s role to advance artificial intelligence in any way possible. He has the skill to put things together from different places. When his parents died, Alberto realized he had a responsibility to use his experience to better other people’s lives. Struggle, doubt, and fear is part of life. However, we need to keep moving forward with artificial intelligence. Alberto has been able to talk to young people about artificial intelligence. He is fascinated with all conversations and curious about everything.

Enjoy the show!

We speak about:

[00:30] About Alberto Roldan
[05:20] Why did you pursue law first?
[07:30] How do you fuse so many different technologies?
[08:40] What led you to your mission?
[11:00] What lessons have you learned on your mission?
[15:00] How does AI affect us?
[20:45] Should companies encourage creativity?
[25:15] How will AI merge with consciousness?
[28:35] What would you like to see happen with AI?

Resources:

Alberto’s LinkedIn: https://www.linkedin.com/in/alberto-roldan-4571ba3/

Quotes:

“People need to take responsibility to find what is real.”
“Have empathy for those who are less fortunate for you.”
“Let love and empathy guide us.”
“I became used to look[ing] at the world as frequencies, not just binaries”
I'm curious about everything
“I think that in our busyness of our daily lives that we all have, we really should take time think of how these changes are affecting us instead of riding the wave”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Today, industry experts participate in a discussion around analytics in the Fast-Moving Consumer Goods Space (FMCG). We open with a discussion of how millennial preferences have completely changed the FMCG market. Millennials are not typically drawn to one big brand; rather, they are more drawn to hyper localization; everyone wants a personalized product. Additionally, 40% of Millennials check online before even going into a store. All of this has significant implications for the FMCG field. Strategies will need to be developed to understand what people want before it reaches the market. We will see much more localized and customer-led strategies. Some easy to implement areas where we will undoubtedly see changes are: promotional spin, customer segmentation, and industry forecasting.

Next, our guests talk about analytic capabilities. We are moving to do fewer things better because shoppers are expecting personalization, but businesses have to continue to be mindful of scale. There are many opportunities for retailers who are hoping to shift to a more centralized model; however, this is only possible if you have reliable data analytics, which enables you to implement things like automatic ordering and work few skews harder. Right now, our biggest challenge in analytic capabilities is how to tie all the data and tools together; we have tools for manufacturing, customer interest etc., but nothing that really ties all those insights together. The next step is to find ways to use advanced data on consumer opportunities at a localized level without losing the power of a company’s scale actually to bring things to the market.

We wrap up the conversation with a discussion on promotional effectiveness. Here, the clunky companies are really at a disadvantage. They need to become more nimble and agile but hold the scale that makes them so profitable. Looking ahead, it's clear that purchasing behaviors will change; everyone will shop online. We need to get sophisticated and to the point where we can use promotional analytics to capture metrics like when are people shopping online the most and when are they really spending money.

Enjoy the show!

We speak about:

[02:25] Introduction to Analytics in the FMCG Space
[9:45] Key Learnings in Analytics Capabilities
[19:30] Opportunities for Retailers
[42:00] Data Analytics and Promotional Effectiveness

Quotes:

“Data is great, but only if you know what to do with it.”
“The data assumes that consumers are rational, I assure you, they are not!”
In promotional analysis, the art has to meet the science.”

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Today Felipe gives us a brief but holistic introduction into Data Science. We discuss myths, Felipe’s journey into the data science world, and demystify some of the field's elements. When Felipe started his career, he did not know anything about data science but found himself in the area of machine learning at the Australia New Zealand Banking Group (ANZ). Eventually, Felipe found himself pioneering the first data-driven strategy at ANZ.

We learn that one of the biggest myths about data science is the fear that AI will eventually replace humans. Dispelling this myth is possible when we develop a wider understanding of what machine learning actually can and cannot do. First, we need to understand how machine learning works. We learn that machine learning updates the foundational data science process of input (data), algorithm (instructions), and output. Instead, it takes the job of developing the instructions, algorithm, or recipe off of the data scientist. Instead, an algorithm is created based on the data and feedback that is given to the machine. Felipe then dives into an explanation of the two basic types of algorithms, classification, and regression algorithms. Classification algorithms organize categories, and regressions deal with the likelihood of outcomes by shooting out a number from 0 to 1.

Felipe spends some time breaking down concepts like AI and decision trees and shares some history of the development of algorithms. Bias data and the importance of understanding how algorithms can be biased are discussed. The power of the decision-making capacity of humans working with machines and data is highlighted. Felipe sees the potential of marrying human judgment and experience with algorithms and data as a game-changer in many areas including the medical field. We close with a Q&A and resources for people hoping to get started in data science, including programs that equip you with the skills and knowledge to get started in the field and include a mentorship program.

Enjoy the show!

We speak about:

[01:25] About Felipe
[04:55] What Can Data Science Do & How Does It Work?
[17:00] Algorithms
[32:00] Key Terms and Decision Trees
[46:00] Coupling Machine Learning and Humans
[60:00] Q&A and Resources To Get Started

Quotes:

“In today’s world, everyone should know how to read and write, in tomorrow’s world, everyone should know how algorithms work”
“Machine learning can supplement thinking, show you things you haven’t considered, and give you a better perspective that allows you to make better decisions”.
“Are humans going to be replaced? Will it always be a combination? That’s up to you.”

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

In this episode, Felipe reflects on the past year with Data Futurology. One of the key themes is organizational outcomes. There is a difference between what people find interesting in the data science role and what organizations need from their data scientists. Organizations want to make better decisions based on data. However, data scientists like playing with shiny new toys and using the latest technology. People think about leaving their jobs because it feels like they have used all the techniques that companies have to offer. Mainly, people want to collect algorithms. However, leaders say they have made an impact without knowing all of the algorithms or cutting edge methods. It can be boring to focus on the same algorithms.

Organizations need to focus on creating value by solving business problems. If you focus your efforts on solving traditional business problems with new approaches, it’s like a musician learning how to play instruments better. Whereas, data scientists want to learn how to play lots of different instruments. Learning the ins and outs of one algorithm will unlock various opportunities to create value for the organization. Plus, having a more in-depth understanding will allow for more creative applications. You don’t need more data or computing power; you need to be smarter with how you approach problems. Aim to be outcomes-driven and be better at using traditional tools.

“I fear not the man who has practiced 10,000 kicks once, but I fear the man who has practiced one kick 10,000 times.” -Bruce Lee

Enjoy the show!

We speak about:

[04:30] Introduction to organizational outcomes
[08:25] Creating value by solving business problems
[15:15] Change the way you approach problems

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Irina is Chief Product Officer for Kinetica. Irina has over a decade of product management experience across a variety of sectors, including enterprise software, networking, hardware, IoT, SaaS, and Cloud. Irina joins Kinetica from Riverbed Technology, where she held a variety of leadership roles including Vice President of Products and Strategy for the Service Provider Business and Vice President of Product Management for Steelhead, Riverbed's flagship product.

Enjoy the show!

We speak about:

[00:30] About Irina Farooq
[01:20] How did you get started in data?
[02:50] Why did you switch from engineering to product management?
[03:50] What did your career look like after switching to product management?
[04:35] How did you learn the craft of being a product manager?
[06:20] What type of products were you working on at the time?
[08:10] What kept you going during the tough times?
[11:25] What has your career looked like after Oracle?
[16:00] What challenges did you help customers overcome?
[18:15] How do the improvements work?
[19:50] What is feature engineering?
[24:15] How do you manage the lifecycle of operational models?
[30:45] What type of information do you keep track of?
[31:20] About Kinetica
[34:00] What does a day on the job look like?
[35:00] What are your biggest challenges?
[37:00] Why did Kinetica decide to go to Australia?
[39:30] How does tiered storage work?
[42:15] How much has the platform changed?
[44:45] What are you most proud of?
[49:00] What do you think about challenges in the data space?
[50:00] What is a piece of advice for the listeners?

Resources:

Irina’s LinkedIn: https://www.linkedin.com/in/irinafarooq/

Kinetica: https://www.kinetica.com

Kinetica on Twitter: https://twitter.com/Kineticahq

Kinetica on LinkedIn: https://www.linkedin.com/company/kinetica/

Kinetica on YouTube: https://www.youtube.com/kineticadb/

Quotes:

“Learning and self-improvement kept me going during the tough times.”
“The biggest transformation has been being deployed in some of the world’s largest enterprises.”
“Figure out what your superpowers are and focus on those things.”
“You can’t just fit the mold with everything.”

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

During this episode, Felipe talks about how to find and double down on your strengths. Instead of working on your weaknesses, Felipe says that we should do more of what we are already good at. Spend more time thinking about the things that you are talented in. Felipe is good at going and helping teams. Eventually, he makes himself redundant by doing these three things:

Sharing knowledge openly, quickly, and effectively.
Create a system that is self-organizing and self-sustaining.
Helping individuals learn and improve as soon as possible.

When working with individuals, find out what their strengths and weaknesses are. Also, learn where they want to go and what they want to know. Then, you will need to understand how they can learn and the ways they can get better. For example, Felipe caught up with a data scientist; he was working on learning his perceived strengths and weaknesses. However, the skills he was working on didn’t line up with a long-term vision for his career. That’s why it’s essential to have a vision in mind.

Enjoy the show!

We speak about:

[01:30] How Felipe makes himself redundant
[04:40] Working with individuals in the workplace

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Terence is a data scientist, software engineer and bioinformatician with over a 15 years of experience working on software solutions to big data related problems in industries as diverse as finance & investment banking, construction, manufacturing, healthcare and retail sectors. Terence is the CEO of Growing Data, a Melbourne based Data Science and Data Engineering consultancy, working with clients such as ANZ, CSL, Metricon and the Victorian Government.

Enjoy the show!

We speak about:

[00:30] About Terence Siganakis
[02:30] Marketing Growing Data
[04:50] Working with the government
[06:30] The numbers behind machine learning
[10:00] Why does governance have negative connotations?
[11:20] How do you minimize the work upfront?
[15:55] How are you tackling big problems?
[19:15] How do you stay engaged?
[20:40] The way organizations interact with data
[22:40] How does Growing Data work?
[25:00] Data in the health industry
[31:00] Working in finance, health, and construction
[34:05] Driving innovation

Resources:

Terence’s LinkedIn: https://www.linkedin.com/in/terencesiganakis/

Growing Data: https://growingdata.com.au

Quotes:

“Think about the business outcome.”
“Have confidence that what you are building is going to be production-ready when it reaches its targets.”
“No one wants to come up with a solution and have nothing happen with it.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

During this special episode, Felipe gives a presentation on what data science can do for business. The CEOs in the audience were from all sorts of industries with companies of different sizes in various stages. Business leaders can use AI to offer new solutions to their customers. Felipe explains machine learning – humans give the input and the output. The machine will do the calculations in-between the input and the output. We can automate a lot of processes using machine learning – it can help us make better decisions, understand human bias, and help us create better businesses. Very few algorithms are 99% accurate. However, they will allow your business to automate decision making and give more insight to make better decisions. Next, Felipe talks about building a data science team. Once you pay people enough, they care about three things: autonomy, mastery, and purpose. Companies are focusing on creating data science products. Most of the value is designed this way; it is very enticing for a data scientist to work in production. Every process that you automate is creating a lot of data that you should be capturing. The ways you can get data is by buying it, scraping it from the web, or collecting it within your organization. Getting information from a different division of your business will improve what you can do internally. Later, Felipe speaks about design-thinking. Find problems that your customer cares so deeply about that they have hacked together a solution. When you find something they care about that has business value, it’s a great place to start. Stay tuned as Felipe takes questions from the audience.

Enjoy the show!

We speak about:

[01:15] About Felipe

[03:30] How does machine learning work?

[07:30] The algorithm will provide insight

[09:30] Building a data science team

[13:30] How to create data

[15:20] The difference between lake, warehouse, and swamp

[18:15] About design-thinking

[20:55] The key points

[22:00] Audience questions

Resources:

Idio: https://idio.ai

Quotes:

“When working with data scientists, get them away from thinking everything needs to be perfect.”

“Machine learning can be used for automation and to make better decisions.”

“Data scientists need to think about the outcome instead of research.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Leigh McCormack is a thought leader with 10+ years of experience in developing and applying analytical solutions to complex healthcare problems. She has a deep understanding of data science concepts, methods, and technologies and how to appropriately apply each to accurately and creatively meet objectives. Leigh also has experience building analytic and technical teams from their genesis, through organizational design, and into maturation to the point of demonstrable business value. Leigh is the Chief Executive Officer of Base Camp, a healthcare analytics platform that leverages geospatial analytics, natural language processing, and machine learning to curate actionable social determinant of health insights.

Enjoy the show!

We speak about:

[01:00] How Leigh got started in data
[05:00] What data did you look at in clinical trials?
[06:00] How can healthcare data be used and reused?
[08:30] What type of analytics did you look at in the health care industry?
[09:40] How detailed can you go into your findings from the data?
[12:20] Were you able to measure health outcomes?
[14:15] How was your journey advancing in the company?
[16:00] Did your leaders see the importance of data?
[20:10] How do you go on a data journey with your team?
[22:00] Where else has your career taken you?
[24:25] Do you need to sell the social side of data to organizations?
[25:55] What type of projects have you been working on this past year?
[27:40] How have career transitions been for you?
[29:30] What are your other positions, and how do you balance your time?
[33:30] Where does your drive come from?
[35:40] How do you balance family life and work?
[36:40] How did you come up with the idea to start your own company?
[39:20] What part of your career set you up to tackle your startup challenges?
[40:45] How do you spend your time at work?
[42:11] What is a piece of advice you would give our listeners?

Resources:

Leigh’s LinkedIn: https://www.linkedin.com/in/leigh-mccormack-939a3222/

Base Camp Health: https://basecamp-health.com/

ChaTech: https://chatechcouncil.org/

Women in Analytics: https://womeninanalytics.com/

Quotes:

“Healthcare organizations don’t understand the amount of data that they are sitting on.”
“Not only do I have to live inside my data science framework, but now I have to master my marketing framework and a sales framework.”
“There is something new every day in data science and artificial intelligence.”
“Don’t stay in your lane.”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

During this special episode, Felipe joins a panel at the Australian Taxation Office. Felipe speaks on four main topics:

Improvements in the data industry.
Felipe’s views on privacy.
Quantum computing and how it will affect machine learning.
New developments on AI and how they are impacting jobs.

So much of the analytic work we do is to benefit the organization. How do you also ensure data analytics are helping the customer? It needs to be done in a way that is presenting meaningful insight for the individual. For instance, car technology today comes with a range of sensors and cameras that are making decisions. However, this data isn’t being collected and reported back to the customer. Grocery stores are also collecting data and using it to make predictions. For example, stores know when their consumers will need to buy toilet paper. The stores should be sending a reminder to their consumers by reusing the data they already have.

Then, Felipe discusses ethics in artificial intelligence. Algorithms are consistently being deemed racist and sexist. We need to realize that the algorithm only learns the bias because the bias is in the data. When we find these errors, we can have tough conversations and improve the algorithms.

Later, Felipe talks about how AI is impacting jobs. For instance, AI is now creating custom videos and articles for popular news websites. On one side of the argument, we are going to have a much more efficient economy. While on the other hand, many people say we are going to lose jobs. Felipe says that most jobs will be enhanced by AI instead of being replaced by AI.

Enjoy the show!

We speak about:

[02:55] Making data analytics benefit the customer
[11:30] Ethics in artificial intelligence
[14:20] Felipe’s views on privacy
[16:30] Quantum computing and how it will affect machine learning
[19:00] How AI is impacting jobs

Resources:

ATO: https://www.ato.gov.au

Quotes:

“The same data that is prepared for the organization can be taken to benefit the consumer.”
“The data is biased because it is a representation of our world.”
“I think that the discussion around privacy heightens when we move it away from value.”
“Jobs will be enhanced by AI, not replaced by AI.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

During this special episode, Felipe speaks at the IAPA National Conference. His presentation focuses on making analytics matter for your customers. More specifically, Felipe speaks on these topics:

About organizations making a mindset shift.
How analytics professionals can leverage the work they do.
The ways we can add value to the end customer.

Many companies claim they put customers first, but how do they show it? Companies have heaps of data on their customers. The next wave of companies that do great will actually show customers that they care about them. There are three simple ways to use analytics for your customers:

Benchmarking. For instance, when a student gets a test back. What percentage of students did better them? Benchmarking is often overlooked, yet it can provide excellent value to the customer.
Predictions and forecasts. We have loads of data on our customers to benefit the business. However, automated tools can redirect the data and use it for the benefit of the customer.
Key drivers. Everyone wants to be better. We can give our customers feedback on how they can be better and achieve the goals that they have.

Felipe uses grocery stores as his first example of how to implement these three steps. If the app sees you are buying pasta and sauce, they can offer their customer free garlic bread as a gift from the store. It’s a personalized and unexpected gift. The algorithms will also know when their customers are going to need toilet paper. This information is valuable to the company. It can be valuable to the customer because they can send a reminder to the consumer’s app.

What do customers want? They want some perspective in their lives. What’s something about the customer you can inform them about that they don’t already know? Stay tuned as the audience asks questions to Felipe.

Enjoy the show!

We speak about:

[01:40] About Felipe
[04:10] Developing a relationship with your customer
[06:30] Three ways to use analytics for your customer
[12:50] Grocery store example
[15:50] Car example
[20:55] Business owners can also use analytics
[24:30] What do the customers want?
[27:15] Questions from the audience

Resources:

IAPA: https://www.iapa.org.au/advancing-analytics

Quotes:

“Five to seven percent of the data that is being captured is actually getting used.”
“We can get more intimate with our services once we focus our value on the customer.”
“Start with the data that you already have from the customers that you already have.”
“There is a risk of being creepy when you have too much data on someone.”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Barrett Hasseldine has held several leadership roles within the field of Data Science / Analytics. With a BSc majoring in Mathematics and an MBA, he is passionate about bringing mathematics and business closer together. In particular, he loves converting business problems to math problems, guiding analysts to solve the math, then converting the mathematical solution into a set of business actions that drive quantifiable business value. During his career, he has delivered analytic solutions in the domains of Credit, Fraud, Marketing, Politics, and Operations.

Enjoy the show!

We speak about:

[00:30] About Barrett Hasseldine
[01:20] How did you get started in the world of data?
[02:55] Why did you pick operations research?
[08:55] Do you find that people are thinking about business decisions affecting the model?
[10:30] When were you able to see business problems in the math problems?
[15:20] How do you turn the work environment into a positive feedback loop?
[18:30] How did the opportunity come for you to step into management?
[21:55] What would you change about your management techniques now?
[25:00] How are you open to feedback?
[26:45] How do you bring feedback out from people?
[28:40] What do you think about imposture syndrome?
[29:25] How did your career evolve after becoming a manager?
[33:55] What would have made you better prepared for management?
[39:30] What does the process look like when creating a new product?
[42:00] How do you work with other teams?
[42:50] How do you measure the demand for a new product?
[49:35] How have your career goals changed?
[52:00] What is a piece of advice you have for the listeners?

Resources:

Barrett’s LinkedIn: https://www.linkedin.com/in/barrett-hasseldine/

Manager Tools Podcast: https://www.manager-tools.com/all-podcasts

Quotes:

“People don’t follow equations.”
“You learn from the people that you’ve had as bosses in your past, and you learn from your own experience.”
“I’ve always been a fan of continuous learning. I’m open to feedback and use it to change continually.
“The absence of imposture syndrome is an issue.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Mary L. Gray is a Senior Principal Researcher at Microsoft Research as well as an E.J. Safra Center for Ethics Fellow and Berkman Klein Center for Internet and Society Faculty Affiliate at Harvard University. Mary also maintains a faculty position in the School of Informatics, Computing, and Engineering with affiliations in Anthropology and Gender Studies at Indiana University. Mary, an anthropologist and media scholar by training, focuses on how everyday uses of technologies transform people’s lives.

Enjoy the show!

We speak about:

[00:30] About Mary L. Gray
[04:25] What’s it like in the Microsoft Research Center?
[06:25] What surprised you the most about researching with Microsoft?
[08:40] Is your work focused mostly in the United States?
[10:10] Is your previous work focused on the social side of technology?
[14:40] What other interesting viewpoints did you learn during your research?
[24:00] How are people’s work lives being shaped by AI?
[35:30] How is automation going to affect the execution of algorithms in specialized fields?
[42:40] How do you see AI evolving in different countries?

Resources:

Mary’s Website: https://marylgray.org

Mary’s Twitter: https://twitter.com/marylgray

Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass

In Your Face: Stories from the Lives of Queer Youth, Queering the Countryside: New Directions in Rural Queer Studies

Out in the Country: Youth, Media, and Queer Visibility in Rural America

Quotes:

“I love studying gender and sexuality because it is so intimate.”
“If we are constantly interacting with each other, we can constantly transform into different senses of who we are.”
“When you introduce new technologies, it is shaping conversations.”
“The work of data science is a global project.”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Scott is a firm believer in “making your data do the work,” and he has enlightened countless business executives to the value of proper data management by focusing on the strategic rationale and business alignment rather than technical implementation and system integration. Scott is more of the strategic WHY than the technical HOW. He has spent over two decades guiding Tech Brand owners to leverage their reference data and taxonomy assets. In a variety of strategic marketing, GTM, innovation, and consulting roles, Scott has worked with some of the world’s most iconic business data brands, including Dun & Bradstreet, Nielsen, Microsoft, Kantar, NPD as well as start-ups such as Qoints and Spiceworks.

Enjoy the show!

We speak about:

[00:30] How Scott started in the world of data
[04:30] How did you develop your engaging videos?
[07:50] How have you seen data change over time?
[11:30] What are people not understanding about MDM?
[16:35] What are the classic pitfalls?
[19:45] Is master data similar to data engineering?
[21:00] Do people struggle to talk about data as an asset?
[25:50] How are you having the best time in your career right now?
[30:55] What’s the philosophy behind your content?
[38:30] How did you land on the Data Whisperer and Meta Meta Consulting?

Resources:

Scott’s Website: http://metametaconsulting.com

Scott’s YouTube: https://www.youtube.com/channel/UCVQ1YhjNqc77GVsb3Xs4tvw

Scott’s Twitter: https://twitter.com/stdatawhisperer

Scott’s LinkedIn: https://www.linkedin.com/in/scottmztaylor/

Quotes:

“You need data management first before you do business intelligence.”
“I’m the sous chef in the back, making sure we have the right ingredients.”
“Do upon your data as you would have it do upon you.”
“Master data is your most important data.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Emrah Gultekin is the co-founder and CEO of Chooch. Chooch AI is a complete visual AI platform with an API, a dashboard, and a mobile SDK. Combining computer vision training with machine learning, Chooch offers object recognition and facial authentication, with autonomous labeling, data collection, neural network selection, and more. Chooch is used in the media, advertising, banking, medical, and security industries.

Enjoy the show!

We speak about:

[00:30] How Emrah started in the world of data
[03:00] What is the inspiration behind your company?
[05:00] How did you meet your co-founder?
[06:45] How do you tackle disagreements with your co-founder?
[08:00] Did you de-risk your life from the start-up?
[11:00] About Emrah’s products
[14:00] Case study examples
[20:20] What makes Chooch different?
[23:30] Do you have methods to prioritize your labeling?
[30:40] What has surprised you the most about Chooch?
[33:40] When did you decide to move to Silicon Valley?
[34:30] What are you working on now?
[36:15] Advice for the listeners

Resources:

Emrah’s LinkedIn: https://www.linkedin.com/in/emrah-gultekin-6123ab1/

Chooch: https://chooch.ai

Quotes:

“We automate the labeling process – that has been a key thing to scale.”
“No data is perfect.”
“We have to understand that bias is a natural state.”
“The more you know about something, the more critical you will be.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Nic originally trained as an actuary through the University of NSW, Australia. After a few years he found himself naturally being pulled towards data science through his passion for data and statistics. He gave in and made the switch to data science many years ago and has never looked back! Nic has worked in just about every role you can think of in a data science team, from excited newbie to quickly managing 3 teams across 2 countries. Because of his unique beginnings, Nic has had the opportunity to work in diverse industries such as insurance, banking, agriculture, and online advertising and is therefore comfortable turning his hand to any industry. He has worked with some of the largest banks and insurance companies in Australia all the way down to small startups of 4, always helping them to drive value with data. Nic is passionate about the data science community and regularly produces content aimed at inspiring the next generation of data scientists. He is a regular speaker at data science events.

Enjoy the show!

We speak about:

[00:20] How Nic started in the world of data
[09:45] Nic’s experiences as a consultant
[16:15] The decision to homeschool his kids
[20:45] When Nic started his company
[23:20] Staying hands-on with his team
[25:50] Starting a business with his wife
[30:50] What to do if monthly reports are taking too long
[37:20] Bouncing between city and country
[45:15] Appetite for trying new technology
[49:50] Do you see companies in Australia doing remote work?
[55:00] Nic’s proudest moment in his career
[56:30] What are the biggest challenges in the data science space?
[59:30] Where do you see data science going in the future?
[61:00] What are you talking about at your meetup?
[62:00] Do you have advice for data scientists?
[65:45] What advice do you have for a younger Nic Ryan?

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Tomer Shiran is the Co-Founder and CEO of Dremio, Dremio is the Data-as-a-Service Platform company. Created by veterans of open source and big data technologies, and the creators of Apache Arrow, Dremio is a fundamentally new approach to data analytics that helps companies get more value from their data, faster. Dremio makes data engineering teams more productive and data consumers more self-sufficient. Tomer Shiran previously headed the product management team at MapR and was responsible for product strategy, roadmap, and requirements. Before MapR, Tomer held numerous product management and engineering roles at Microsoft, most recently as the product manager for Microsoft Internet Security & Acceleration Server (now Microsoft Forefront). He is the founder of two websites that have served tens of millions of users and received coverage in prestigious publications such as The New York Times, USA Today, and The Times of London. Tomer is also the author of a 900-page programming book. He holds an MS in Computer Engineering from Carnegie Mellon University and a BS in Computer Science from Technion - Israel Institute of Technology.

Enjoy the show!

We speak about:

[01:50] How Tomer started in the data space
[03:35] What was it like running your own business?
[04:50] What did you think would happen with ePassportPhoto?
[07:20] Takeaways from MapR
[09:35] What was the process of starting Dremio?
[10:55] How did you gauge how much product development needed to be done?
[12:20] Where did you start with your hiring process?
[13:00] What have been some of the pivotal moments for Dremio?
[14:35] What does Dremio do?
[16:00] What is the semantic layer?
[20:00] Who are the users?
[23:30] What are the data masking capabilities?
[25:10] How has the journey been for you personally?
[28:35] What challenges are you facing right now?
[30:00] About Tomer’s teams
[31:15] The importance of having a sales team
[33:30] How has Dremio changed with the increase of employees?
[34:30] What does the future look like for Dremio?
[35:00] What do international expansions look like for Dremio?
[35:45] What are you most proud of in your career?
[36:20] Any lessons from your failures?
[37:45] Advice for future entrepreneurs
[40:00] Future challenges in the data space
[41:45] A piece of advice for the listeners

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Lasse Rouhiainen is a best-selling author and international expert on artificial intelligence, disruptive technologies, and digital marketing. Finnish in origin but based in Spain, Lasse focuses his work on investigating how companies and society, in general, can better adapt to, and benefit from, artificial intelligence. Lasse has given keynote presentations, seminars, and workshops in more than 16 countries around the world and holds frequent conferences at several universities internationally. He has also provided training to thousands of students and businesses through online e-learning courses.

Enjoy the show!

We speak about:

[00:15] How Lasse started in the world of data
[01:45] Why is there a lack of information about AI?
[03:15] What are some ethical concerns with AI applications?
[05:30] What are the main things people do not understand about decision-making algorithms?
[07:00] Where does the bias come from?
[08:20] What are the differences between human-centered AI and consumer-centered AI?
[10:40] What are your views on different organizations benefiting from AI?
[13:50] How have you seen the adoption of AI in the education sector?
[18:30] What inspired your book on education?
[21:30] What was the process behind your book on AI?
[28:30] What are the main topics you speak about during your keynotes?
[36:40] How does the ROYBI work?
[37:40] What are you most proud of?
[38:15] What challenges are you thinking about?
[39:50] What excites you most about AI?
[40:30] How has failure set you up for greater success later on?
[42:20] What are future challenges in the data space?
[46:30] How are the universal basic income trials in Finland going?
[47:50] How have you gotten better at prioritizing your time?
[50:30] What is a piece of advice you want to leave for the listeners?

Resources:

Lasse’s LinkedIn: https://www.linkedin.com/in/lasserouhiainen/

Lasse’s Website: https://www.lasserouhiainen.com/

MyData: https://mydata.org/

The Future of Higher Education: How Emerging Technologies Will Change Education Forever

Artificial Intelligence: 101 Things You Must Know Today About Our Future

ROYBI: https://roybirobot.com/

Rise of the Robots: Technology and the Threat of a Jobless Future

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

The Future Of Higher Education: How Emerging Technologies Will Change Education Forever

Artificial Intelligence: 101 Things You Must Know Today About Our Future - Updated Edition for Post-Covid-19 World

Rise of the Robots: Technology and the Threat of a Jobless Future

Dr. Ian Oppermann is the NSW Government’s Chief Data Scientist and CEO of the NSW Data Analytics Centre. Ian has 27 years’ experience in the ICT sector and, has led organizations with more than 300 people, delivering products and outcomes that have impacted hundreds of millions of people globally. He has held senior management roles in Europe and Australia as Director for Radio Access Performance at Nokia, Global Head of Sales Partnering (network software) at Nokia Siemens Networks, and then Divisional Chief and Flagship Director at CSIRO. Ian is considered a thought leader in the area of the Digital Economy and is a regular speaker on “Big Data” and broadband-enabled services.

Enjoy the show!

We speak about:

[02:00] How Ian started in the world of data
[07:20] Ian’s professional background
[10:00] Ian’s role as NSW Government’s Chief Data Scientist
[15:10] The skills that have helped Ian succeed in his role
[19:25] Running an effective ideation workshop
[23:45] Should you get an MBA?
[33:30] Developing risk frameworks
[36:40] What Ian is most proud of

Resources:

Ian’s LinkedIn: https://www.linkedin.com/in/ianoppermann/

Quotes:

“I have the ability to look impossible challenges square in the eye and not blink.”
“Sometimes a slightly crazy idea which you can then find a reasonably sensible idea is a great way to tackle challenges.”
“I don’t think an MBA is for everybody.”
“Data standards are really starting to get interesting.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

In this episode, Felipe runs through 12 trends he has noticed developing in AI. The sector field is in a constant state of change and is become more and more integrated into fields outside the data space. Felipe reminds us that through this development it has become exceedingly clear that data is our most precious asset. We need to treat data carefully and prioritize traceability and maintain high-quality data. Felipe also notes that while we begin to rely heavily on AI we must also be mindful of the importance of AI ethics and beware of bias. It is no secret that decisions are being made that are racist and sexist, however, the algorithm itself is not the problem, the data that is feeding the algorithm needs to be more closely monitored for bias. Felipe discusses some challenges around data and human capital such as the need for more data engineers, building citizen data-literacy capacity and looking at the reliance on AI and data over human data scientists.

A few trends Felipe identifies are centered around the prevalence and ever-broadening capabilities of AI. Moving forward all new phones will have a chip dedicated to machine learning. Felipe notes that now there are some significant developments in machine learning centered around unsupervised learning, auto-machine learning, and correlation and causation that will mean more powerful AI. Consumers can soon expect to benefit more holistically and tangibly from insight-powered by AI, while companies are developing entire departments to use AI to use internal and external data to find threats in their competitive landscapes.

Enjoy the show!

We speak about:

[02:25] Tips #1-6: Data, AI Ethics & Data Engineers
[19:15] Tips #7-12: Business & AI, Machine Learning
[27:00] Recent Developments in AI

Quotes:

“As data scientists, we need to be empathic, non-judgmental, and be able to work with people that have complementary skills.”
“The sooner you can get rid of the judgment, the sooner you can make a difference in more people’s lives.”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

David Thomas is an executive with significant experience in strategy, data & analytics, and multiple functions that enable organisations. Since 2016, he has been responsible for BNZ's Information Strategy, establishing a market-leading position with a strategy to deliver actionable insight and enhance customer experiences. Change has been a reoccurring theme of his career, including business turnaround, transformation, the introduction of new competencies / operating models, and to enable growth. David's professional experience shows a proven track record of delivering sustainable results - frequently fueled by insight and strong execution.

Enjoy the show!

We speak about:

[00:30] About David Thomas

[05:50] What did you learn from your thesis?

[18:00] What are the reasons people stop having a close interaction with the data?

[23:10] What are the components of a good CDAO?

[33:15] How do you keep large teams productive?

[38:00] Advice for future data leaders

Resources:

David’s LinkedIn: https://www.linkedin.com/in/davidthomasnz/

Quotes:

“If you can measure things, then you can get better.”

“You can never underestimate the language.”

“Tech is moving so fast; you can’t do it on your own. Partnering is so important.”

“You need to partner for thought leadership.”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

We are RUBIX. - one of Australia’s leading pure data consulting companies delivering project outcomes for some of the world’s leading brands.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Send in a voice message: https://anchor.fm/datafuturology/message

Beverly has spent over twenty-five years leading and executing Marketing Analytics and Modeling through corporate, consulting, and academic experience. She leads the Data Science Practice for RelationalAI for Atlanta, and serves several companies with data science strategy, insights, solutions, and training. In her role heading Georgia Tech's Business Analytics Center, Beverly managed business community engagements, as well as student experience aspects of the Center. Beverly earned a PhD in Marketing Science, a Master of Science degree in Analytical Methods, and a Bachelor of Business Administration degree in Decision Sciences.

Enjoy the show!

We speak about:

[03:15] How Beverly started in the data space
[04:30] Should you get experience before getting a PhD?
[06:50] Beverly’s career path
[08:35] When did you start each project you’ve been working on?
[09:00] What surprises you the most about the nonprofit sector?
[10:00] How was the process of starting a nonprofit?
[11:55] What is the recruiting process for your nonprofit?
[13:40] How did you get started with a podcast?
[16:40] How has the experience been with podcast interviews?
[17:50] How executive education has been for Beverly
[19:00] How do you describe the data journey for executives?
[21:30] What about the golden gut?
[23:30] Advice for changing workplace culture to embrace data
[25:20] Do some executives want data to make all their decisions?
[27:00] About Beverly’s consulting work
[29:30] What type of industries have you been working with?
[33:00] What makes a good strategy?
[36:00] About the enablement pillar
[31:40] About the impact pillar
[46:00] What are your views on where data access is going?
[48:20] How has collaboration benefited your work?
[51:00] What would you say to people who are afraid of the amount of demand they will get for connecting with people too early?
[52:45] Beverly’s thoughts on diversity in data science
[57:00] How did you develop your communication skills?
[59:30] How do you improve your communication skills?
[62:30] What is your advice for people just starting in this space?

Resources:

Beverly’s LinkedIn: https://www.linkedin.com/in/drbeverlywright/

Beverly’s Twitter: https://twitter.com/drbdub?lang=en

Beverly’s Podcast: https://tagdsa.org/tag-data-talk/

RelationalAI: https://www.relational.ai/

ATLytiCS: https://www.meetup.com/ATLytiCS-Analytics-For-Good/

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

In this episode, Felipe spends time discussing empathy as the core of human relationships. Felipe believes to be a leader in the data science space; half is deeply rooted in the people side. Having empathy is the crucial first step to building relationships with stakeholders. Our stakeholders are scared; today data science is math heavy and dominating the business side of operations. The discipline of data science is taking over organizations and trying to tell people what to do. Enabling people to make better decisions, automating work, and uncovering opportunities are goals of data science. However, stakeholders may not understand the complicated methods of data science and will feel incompetent about this new method of solving problems. Building relationships with stakeholders will require the data scientist to meet people where they are. These conversations will allow each party to gain knowledge from the other and use it to give back to the business. Instead of starting with the steps you took to get the results, start with the objective and give something people can get excited for. Then, go to the punchline and say how you improved and present them with the outcome. Lastly, provide a few details about the process and wait to see if the audience has more questions, or if they are happy with the result.

Enjoy the show! We speak about:

[03:05] Empathy: people are scared

[08:00] Outcome: meet the stakeholders where they are

[12:00] Communication: taking stakeholders through the process

Quotes:

“As data scientists, we need to be empathic, non-judgmental, and be able to work with people that have complementary skills.”

“The sooner you can get rid of the judgment, the sooner you can make a difference in more people’s lives.”

Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Nufar Gaspar is a Data Science Vertical Manager at Intel focusing on creating a machine learning and big data solutions for the various R&D teams at Intel. The teams in her vertical create a vast range of data-science solutions on Hadoop and Spark-based platforms. The range of their solutions spans running algorithms to compile test lists to optimize resource consumption vs. test quality; solutions for finding hidden CPU bugs; through adv. methods to automate debug process and more. They use a variety of data science techniques from "classical" machine learning and pattern recognition techniques, through optimization & NLP techniques to deep learning and reinforcement learning, and more. Her organization includes teams of data scientists, big data SW developers, and AI specialist's product managers and analysts.

Enjoy the show!

We speak about:

[02:00] How Nufar started in the data space
[10:30] Nufar began to train new engineers
[21:50] Focusing on bringing solutions to production
[24:20] How Nufar picks what to work on
[27:15] Putting algorithms inside Intel’s products
[32:00] The infrastructure behind Intel’s machine learning
[40:30] Returning to work after maternity leave
[43:30] Women in Big Data
[47:50] What excites Nufar most about data science
[49:30] Advice for listeners

Resources:

Nufar’s LinkedIn: https://www.linkedin.com/in/nufar-gaspar-76b7424b/

Nufar’s Talk: https://www.youtube.com/watch?v=cx_2wAP40-g

Women in Big Data: https://www.womeninbigdata.org

Quotes:

“You have to understand the different business processes.”
“We are doing a complete business process redesign from scratch.”
“For us to maintain our leadership position in AI, we have to continue running as fast as possible.”
“Before I left for maternity leave, I had to negotiate with my managers to determine what position I would have when I return.”

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Dr. Mark van Rijmenam is the Founder of Datafloq. He is a highly sought-after international public speaker, a Big Data and Blockchain strategist and author of the best-selling book Think Bigger - Developing a Successful Big Data Strategy for Your Business. He is the co-author of the book Blockchain: Transforming Your Business and Our World, which details how blockchain can be used for social good. His third book, The Organisation of Tomorrow, will be published in Q2 2019. He is named a global top 10 Big Data influencer and one of the most influential Blockchain people. He holds a PhD in Management from the University of Technology, Sydney. His research was on how organisations should deal with big data analytics, Blockchain, and AI. He is the publisher of the ‘f(x) = ex‘ newsletter read by thousands of executives. Enjoy the show!

We speak about:

[01:50] How Mark started in the data space

[02:40] What made you change from being a consultant to an entrepreneur?

[05:50] How has the data space evolved since you began?

[06:55] How do organisations become data-driven?

[08:20] How was getting a PhD after establishing a career?

[09:00] Do you recommend people go back and get a PhD once they have established a career?

[10:20] Do you recommend companies take small steps or significant steps when becoming data-driven?

[12:40] Do you have examples of automating customer service?

[14:45] What are the key barriers holding companies back from becoming data-driven?

[16:00] Are employees excited about becoming data-driven?

[18:40] Why is blockchain challenging to bring into reality for supply chains?

[24:50] How did you manage your time while pursuing your PhD and working?

[27:25] Mark’s inspiration behind Datafloq

[30:55] What are your views on privacy and data?

[34:15] What are you most proud of?

[35:00] What excites you most about this field?

[35:55] Do you have any failures that helped you learn?

[37:10] What are the challenges in the data space?

[39:15] How do you decide what to work on?

[43:00] How have you learned to handle the lows in entrepreneurship?

[43:50] Do you have any advice for our listeners?

Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Visit online.rmit.edu.au for more information

Thank you so much for listening!

Send in a voice message: https://anchor.fm/datafuturology/message

Tony believes that analytic innovation and creativity come from experts directly collaborating with clients on their projects. He joins AlphaZetta with a mission, to create a better way for independent experts to work directly with clients, lowering the costs and opening access to high quality analytics talent to a wider audience. He brings 20 years of deep practical experience globally. He has held senior positions with clients and large consulting firms. He has won several awards for innovation in analytics including as a Finalist for Australia’s Young Businessperson of the Year Awards.

Enjoy the show!

We speak about:

[02:00] How Tony started in the data space
[04:00] What Tony’s journey has looked like
[05:30] Comparing data and analytics in different countries
[08:30] How was the adoption of the data infrastructure in China?
[11:10] How did ICBC gain new users?
[12:25] What are you most excited about?
[14:00] How can you bring data ethics to life?
[18:40] Any surprises when practicing data ethics?
[20:20] How have people jumped on board with data ethics culture?
[22:15] What was it like to build a bank with analytics at its core?
[27:00] About AlphaZetta
[32:20] What problems does AlphaZetta like to tackle?
[34:10] How does consulting work at an analytics company?
[35:30] What surprised you the most with AlphaZetta?
[37:00] Have you been involved in early-stage companies previously?
[39:15] Lessons learned from previous work experience
[41:00] What does the decision-making process look like in analytics?
[42:40] The visions for Volt Bank and AlphaZetta
[44:30] What would you like to be known for?
[47:00] What are the current and future challenges for the industry?
[50:20] What are you most proud of?
[51:25] A piece of advice for our listeners

Thank you to our sponsors:

Fyrebox - Make Your Own Quiz!

RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Angela Wilkins is the founder and Chief Scientist at Mercury Data Science (MDS) where she works with Mercury's portfolio companies to identify solutions for complex data problems. Prior to MDS, Angela was a member of faculty research at Baylor College of Medicine and led projects at the policy think tank, Center of Science and Law. She developed her machine learning knowledge in the biomedical field as part of IBM's Watson AI and DARPA Simplex Project. Angela received her M.S and Ph.D. from Lehigh University, all in Theoretical Physics.

Enjoy the show!

We speak about:

[01:10] How Angela started in the data space
[03:45] Using proteins to understand aging
[07:35] Setting team expectations
[09:15] Challenges at Baylor
[15:15] Using data science to make policy
[18:30] Leading seminars on machine learning and data science
[22:05] TrendKite – putting the right data in front of the PR person
[26:40] The transition from academia into business
[30:15] Rapid fire questions
[35:00] Future data science challenges
[36:10] Do as many things as you can

Resources:

Angela’s LinkedIn: https://www.linkedin.com/in/adwilkins

Mercury Fund: https://mercuryfund.com

TrendKite: https://www.trendkite.com

Quotes:

“We look to see how proteins, drugs, and diseases interact.”
“We came up with an algorithm for aligning protein networks along multiple species.”
“I left Baylor because I needed new problems.”
“Algorithms are useful everywhere.”

Thank you to our sponsors:

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Michael Brand has over 25 years of cutting-edge, international industry experience in advanced analytics, machine learning, artificial intelligence, machine vision, and natural language processing, Dr. Brand’s data expertise is both uniquely wide and uniquely deep. He served as Chief Data Scientist at Telstra Corporation, as Senior Principal Data Scientist at Pivotal, as Chief Scientist at Verint Systems, as CTO Group Algorithm Leader at PrimeSense Ltd (in the machine-vision team that developed the Xbox Kinect), and as Director of the Monash Centre for Data Science in his role as Associate Professor of Data Science at Monash University. He has developed solutions at every scale from on-chip to Big Data, from real-time to high-powered computing, and made industry-defining contributions that have earned him 16 patents (more pending), garnered many prestigious industry and academic awards, and underline $100Ms/pa revenues and $100Ms in valuation for the companies he worked with.

Enjoy the show!

We speak about:

[01:45] How Michael started in the data space
[05:35] Capturing brand new blood pressure data
[09:15] What you buy and eat depends on the weather
[15:10] Working with data science in the Israeli army
[19:40] Engineering approach vs. the scientific method approach
[28:45] When is the deep learning madness going to end?
[31:00] Working at Verint Systems
[36:05] The core of what Michael currently does
[43:20] Tools to ensure secrecy
[47:20] Making strategic decisions with data science
[50:00] Every company needs a data strategy
[54:15] Where does data governance play a role in an organization?
[63:10] The need to start talking about data rights
[69:20] Listener questions
[76:00] Michael has imposter syndrome

Resources:

Michael’s LinkedIn: https://www.linkedin.com/in/michael-brand-b230736/

Otzma’s LinkedIn: https://www.linkedin.com/company/otzma-analytics/about/

Otzma Analytics: https://otzmaanalytics.com

Quotes:

“When you have data that nobody has ever looked at before, you will see stuff that nobody has ever seen before.”
“We are in a world where we are pushed towards thinking of data science as a form of engineering.”
“You can outsource a lot of things, but you should do your own testing.”
“Every data you encounter is different; the value is understanding how that data is different.”

Thank you to our sponsors:

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Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Amy is an Engineer and a multidisciplinary Data Scientist at USAA. She received her B.E degree in Electrical and Computer Engineering at the University of Minnesota and her M.S. and Ph.D. degrees in Electrical Engineering from the University of Texas. Amy is an active promoter for women in STEM and enjoys teaching & organizing community events to increase women's visibility in the field. She previously served as the Women in Data Science Ambassador (WiDS) for the Global Women in Data Science group at Stanford University. She also founded San Antonio Data Science Meetup in 2016.

Enjoy the show!

We speak about:

[02:55] How Amy started in the world of data
[05:40] Amy’s professional background
[07:40] Amy’s Ph.D. changed everything
[13:00] Deciding to become an entrepreneur
[17:45] Having kids inspired Amy to work in healthcare
[25:20] Amy’s passion has kept her motivated
[27:50] Being a female in the data space
[35:40] About the Women in Data Science Meetup
[42:00] Having a work/life balance
[44:25] What Amy is most proud of in her career
[49:30] Amy’s advice for aspiring data scientists

Resources:

Amy’s LinkedIn: https://www.linkedin.com/in/amywdaali/

Amy’s Twitter: https://twitter.com/wdaali999?lang=en

Lucea AI LinkedIn: https://www.linkedin.com/company/lucea-ai/about/

Lucea AI Website: https://www.lucea-ai.com

Women in Data Science: https://www.widsconference.org/ambassadors-2018.html

San Antonio Women in Machine Learning & Data Science: https://www.meetup.com/San-Antonio-Women-in-Machine-Learning-and-Data-Science/events/

IEEE Smart City Summit: https://attend.ieee.org/scs-2019/speakers/

Quotes:

“Data has the ability to help a lot of people.”
“If you can do a Ph.D., you can do anything.”
“I love the fast pace of the industry.”
“You don’t need a PhD to be a data scientist.”

Thank you to our sponsors:

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Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Ru is a graduate from the University of Cambridge, UK who has built six startups in four countries. His primary interest is to build products with social Value. He is also a mentor of Google Launchpad and a senior AI advisor of EFMA Banking group. Ru has been invited to speak at over 60 events from 21 countries. His talks are about sharing his experiences on growing various startups and building products in Artificial Intelligence and Machine Learning.

Enjoy the show!

We speak about:

[01:45] How Ru started in the AI space
[06:50] What surprised Ru about startups
[10:15] Getting data first, then finding customers
[15:00] Ru’s writing career
[18:35] How to live a complete life
[20:40] What success means to Ru
[31:10] Bringing to life the machine learning models
[35:00] Ru’s advisory roles
[39:00] OpenAI
[41:20] Overcoming challenges with data
[44:00] Challenges with user adoption
[50:00] What Ru is most proud of
[51:40] Advice for the audience

Resources:

Ru’s LinkedIn: https://www.linkedin.com/in/mitrar

Ru’s Website: https://www.mitrarudradeb.com

Creating Value With Artificial Intelligence: Lessons Learned from 10 yrs of Building AI Products and Overcoming Data, Adoption, and Engineering Challenges

Quotes:

“I overcome challenges by learning from my failures.”
“Two years ago I would have never thought I would be a good public speaker.”
“Success cannot be defined by external factors.”
“I haven’t been stressed for three years, maybe more.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Creating Value With Artificial Intelligence: Lessons Learned from 10 yrs of Building AI Products and Overcoming Data, Adoption, and Engineering Challenges

Alex Ermolaev has been involved in the software industry for 20 years, including AI-specific experience at Bell Labs, Microsoft, several startups and now Nvidia. He is currently a leading AI software developer and works with groundbreaking companies that are implementing incredible AI solutions across several domains.

In this episode, Alex describes how he started in the data space. Early in his career, he got a chance to work on a lot of data and software products.

Enjoy the show!

We speak about:

[01:50] How Alex started in the data space
[04:55] Alex’s professional background
[10:30] Working for the finance team at Microsoft
[14:55] Business development skills
[18:50] Challenges working with startups
[22:10] Working at Nvidia
[26:20] Successful and unsuccessful AI patterns
[30:00] AI and collecting data
[35:15] How to tackle data problems using AI
[40:30] Exciting uses for AI
[43:15] The execution of new AI programs
[49:00] What Alex is most proud of
[50:20] Be patient and invest in your knowledge

Resources:

Alex’s LinkedIn: https://www.linkedin.com/in/alexermolaev

Quotes:

“The best way to develop knowledge in any area is to experience it.”
“It is easier to sit in an office and assume the world works in a certain way.”
“Don’t be in startups because it’s cool, try and find a path that meets your own needs.”
“Working with startups is a lot of broader outreach and helping the community understand what is possible.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Evan Shellshear has been an entrepreneur for more than a decade, and throughout that time he has always loved getting his hands dirty with building products from scratch and then commercializing them. Evan has a passion for innovation and not just from a managerial perspective but also from a doing perspective. He has a Ph.D. in Game Theory, is published in fields computer graphics to politics, mathematics to manufacturing, and much more. Evan has founded or co-founded over half a dozen companies to commercialize different technologies.

Enjoy the show!

We speak about:

[01:15] How Evan started in the world of data
[09:45] Zoom out to solve technical roadblocks
[12:10] Examples of how Evan zoomed out
[14:25] Why is zooming out a challenge for data scientists?
[18:00] Focus on simplification
[22:45] Taking opportunities that present themselves
[27:00] Measures of success during a project
[31:00] The process of a case study
[36:05] Getting users to adopt new technologies
[40:00] Innovation Tools
[47:20] Evan’s proudest moment
[49:40] Challenges for the future of machine learning
[51:30] Get soft skills

Resources:

Evan’s LinkedIn: https://www.linkedin.com/in/eshellshear/

Innovation Tools: https://amzn.to/2OrrAsj

Quotes:

“Take a step up and over to look at the problem in a new direction.”
“It is in our human nature to overcomplicate things.”
“I need to help the company understand what the true problem is.”
“Take a low-risk approach to solve your client’s problem.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Rachel Fojtik is an Experienced Senior leader in Analytics, influencing change in behaviour, company culture, and improvement with analytics. Managing high performing teams that deliver across a myriad of knowledge areas. She is passionate about delivering information that sees results, using collaborative design and development. A demonstrated history of setting up teams that deliver end to end business intelligence implementations. Cross-industry experience in healthcare, telecommunications, the financial services industry, travel and tourism, and energy.

Enjoy the show!

We speak about:

[01:15] How Rachel started in the data space
[08:40] The motivation behind Rachel’s trailblazing
[11:30] The metrics Rachel was helping optimize
[14:10] Working with the management director vs. operational work
[16:45] Data matching at Diner’s Club
[22:15] Using a minimalist view
[24:45] Find the best way – don’t just stick with what you know
[28:45] If something is well presented, it is more likely to be trusted
[35:00] What is a product manager?
[42:50] An organic governance in the workplace
[46:15] Rachel’s role as Director of Analytics and Performance
[53:00] Building and working on a network
[54:10] Do what you’re passionate about

Resources:

Rachel’s LinkedIn: https://www.linkedin.com/in/rachel-fojtik-78321199/

Quotes:

“I created an input tool where a user could design the layout of their input form.”
“I’ve always tried to go with a minimalist view.”
“If your presentation is way too busy, it is difficult to take a story from that information.”
“Consider where the eye goes first when creating a presentation.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Christoph Molnar is a data scientist and Ph.D. candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. Christoph is passionate about using statistics and machine learning on data to make humans and machines smarter.

Enjoy the show!

We speak about:

[02:10] How Christoph started in the data space
[09:25] Understanding what a researcher needs
[15:15] Skills learned from software engineers
[16:00] Statistical consulting
[19:50] Labeling data
[23:00] Christoph is pursuing his Ph.D.
[29:00] Why is interpretable machine learning needed now?
[31:00] Learning interpretability
[33:50] Accumulated local effects (ALE)
[37:00] Example-based explanations
[39:15] Deep learning
[43:35] The illustrations in Interpretable Machine Learning.
[49:50] How Christoph maximizes the impact of his time

Resources:

Christoph’s LinkedIn: https://www.linkedin.com/in/christoph-molnar-63777189/

Christoph’s Website: https://christophm.github.io

Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/

Quotes:

“Always look at the process when labeling data.”
“After each chapter of my book, I publish it and get feedback.”
“I randomly read a lot of papers and structure the knowledge to fit them together.”
“I express what I want easier with illustrations in my book.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Dr. Bülent Kiziltan is an AI executive and an accomplished scientist who uses artificial intelligence to create value in many business verticals and tackles diverse problems in disciplines ranging from the financial industry, healthcare, astrophysics, operations research, marketing, biology, engineering, hardware design, digital platforms, to art. He has worked at Harvard, NASA, and MIT in close collaboration with pioneers of their respective fields. In the past 15+ years, he has led data-driven efforts in R&D and built multifaceted strategies for the industry. He has been a data science leader at Harvard and the Head of Deep Learning at Aetna leading and mentoring more than 200 scientists.

Enjoy the show!

We speak about:

[02:00] Bülent’s background
[05:50] The transition from astrophysics to business
[08:45] Data leaders need technical experience
[12:45] Academics still need soft skills
[19:20] What data science can offer organizations
[23:50] Addressing causal inferences
[25:30] Recommendations for implementing culture in the workplace
[30:00] How a leader should balance priorities
[36:10] Challenges Bülent currently faces in the industry
[38:15] Hierarchy in the startup space
[40:45] What Bülent loves about data science
[42:45] Future data challenges

Resources:

Bülent’s Website: http://www.kiziltan.org/

Bülent’s LinkedIn: https://www.linkedin.com/in/bulentkiziltan/

Quotes:

“Culturally, I was surprised by the mindset of business leaders.”
“We asked individual members of the data science group to come up with their own ideas that can be implemented in the day-to-day business operations.”
“A diverse team is critically important for the business.”
“All companies will become AI companies in one way or another.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Nick loves playing with, analyzing and visualising data and gets a massive kick out of the change it can bring to people, businesses, and the world. At Lloyd's of London, Nick leads a team of great designers and developers helping people automate processes and get more insight from their data. He gets a buzz from saving others time and surprising them with what surprising insights lie in their data. Out of the office, Nick is keen to mentor or share his experiences and enjoys speaking at events or conferences.

In this episode, Nick discusses some of his favorite projects and describes issues he has faced being part of various teams. When overcoming team obstacles, he listens to every person in the group. If you do not listen to people, then you cannot persuade someone that you are a good guy. Transparency is also essential; explain what knowledge you bring and the processes that you do. People can pick up on integrity, but they can also pick up on suspicion.

Currently, Nick works at Lloyd's of London, the world's leading insurance market providing specialist insurance services to businesses in over 200 countries and territories. He has helped establish a vision and strategy for Business Intelligence within Lloyd's of London and external market published insight. He delivers automated online MI apps to a range of business functions through a roadmap of strategic change while creating a training structure to develop BI analysts across the business.

Nick's data product development team has to work with other teams at Lloyd's of London to create the best products for their customers. They communicate with the innovation team to understand the research. They also work with all the different modelling teams to access their expertise and bounce ideas around with each other. Before working at Lloyd's of London, Nick was self-employed; it taught Nick a lot about customer service, collaboration, and teamwork. Later, Nick explains common mistakes in developing data products, winning global hackathons, and what excites him most about the future of data.

Enjoy the show!

We speak about:

[01:10] How Nick started in the data space
[07:00] The evolution of data warehousing
[11:45] Nick’s favorite projects
[14:45] Navigating team issues
[15:30] Solving problems at Lloyd’s of London
[20:00] Lloyd’s of London customers
[25:40] Interaction with other teams
[30:30] Working for yourself
[35:45] Common mistakes in developing data products
[38:50] Winning global hackathons
[40:20] What excites Nick most about the future of data
[45:45] Nick’s proudest moments

Resources:

Nick’s LinkedIn: https://www.linkedin.com/in/nicholasblewden

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

In this episode, Felipe talks about an aspect of leadership, the one-on-one mentorship and feedback session with people on your team. To start, ask your team member what is on their mind and in general, how have things been going? If they do not have anything pressing they want to discuss in the sessions, Felipe turns to set of twelve questions. The first question is what you are most proud of that you have done since we last caught up? These questions are designed as tools for the team member to recognize the need for self-assessment. What they say is not essential; really, the follow-up questions are more necessary to help them uncover themselves. Next, ask what the team member could have done better since the last time you talked. By allowing them to evaluate and think of improving continually, they can learn faster and become more efficient in their work. The next few questions require a broader perspective and ask about the team as a whole. It should be clear that everything is a team effort, and all member’s ideas are respected and heard by others on the team. After the team questions, head back to questions about the person and ask what they would like to work on or improve? Then, the next issue will take a lot of trust and rapport with your team member, ask what is one thing that is true that you think I do not want to hear? Question nine is how I can help you to do better? This question has taught Felipe that he is good at the big picture but needs to focus on the details and how the team might achieve the big picture. Finally, the last three questions are asking what they like best and least about the organization and if they are happy at the moment. Enjoy the show!

We speak about:

[01:35] Open-ended questions

[02:15] What are you most proud of that you have done since we last caught up?

[03:45] What could you have done better?

[05:30] Questions about the team

[10:00] What would you like to work on or improve?

[11:40] What is one thing that is true that you think I do not want to hear?

[13:10] How can I help you to do better?

[15:00] What do you like the least about the team?

[16:00] What do you like best about the team?

[16:35] Are you happy at the moment?

Resources: Saturday Night Live Quotes:

“One of the best and quickest ways to learn is to evaluate your efforts continually.”

“Understand the human behind the data scientist.”

“You don’t need to be fixing every person’s problem, but everyone needs help every now and again.”

Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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June Dershewitz has spent her career driving analytics strategies for major businesses. She's currently Director of Analytics at Twitch, the world's leading video platform and community for gamers (a subsidiary of Amazon). As an analytics practitioner, she builds and leads teams that focus on marketing analytics, product analytics, business intelligence, and data governance. In her prior life as a consultant, she was a member of the leadership team at Semphonic, a prominent analytics consultancy (now part of Ernst & Young). As a long-standing advocate of the analytics community, she was the co-founder of Web Analytics Wednesdays; she's also a Director Emeritus of the Digital Analytics Association and a current Advisory Board Member at Golden Gate University. She holds a BA in Mathematics from Reed College in Portland, Oregon.

Enjoy the show!

We speak about:

[01:40] How June started in the data space
[08:20] Solving problems in startups
[09:45] Getting a holistic view in the workplace
[11:20] Feeling unsure about owning a piece of work
[15:30] Business intelligence skillsets for data scientists
[19:35] Clear understanding of data roles in the workplace
[20:55] An overview of June’s teams’ structures
[27:10] Managing career transitions with the hub and spoke model
[29:25] Assigning each person a technical buddy
[32:10] The data quality journey
[41:40] Evolution of data quality at Twitch
[48:00] Becoming involved in the data science community
[53:10] Other ways June stays involved in her communities
[55:20] Advice for breaking into the data science field

Resources:

June’s LinkedIn: https://www.linkedin.com/in/jdersh

Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success

Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program

Quotes:

“The thing about being a data person at that time was we just had to figure it out.”
“I was the vice president of everything that needed to get done.”
“At Twitch, we don’t have a clear definition of what a data engineer means.”
“We chose to move to an organization model that is hub and spoke.”
“Data governance can mean lots of things to lots of people.”

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success

Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program (The Morgan Kaufmann Series on Business Intelligence)

Olivia is an internationally known thought-leader, speaker, best-selling and award-winning author, and a data scientist who focuses on the interplay between technology, corporate leadership, and personal growth and happiness. Throughout her career, she has blended analytic tools and holistic organizational practices to deliver successful solutions for her clients. As a lifelong spiritual seeker, Olivia began to see patterns that revealed the importance of love as a driver of business success.

In this episode, Olivia explains why she changed her major to statistics in grad school. Once she completed her degree, she joined a bank in San Francisco. Olivia built a model using logistic regression for the bank. It saved the company 17 million dollars a year in mail expense, making her an instant hero. Her desktop computer had a 500-megabyte hard drive when she was running SAS she couldn’t get into any other programs. Financial services had a vibrant climate for modelling because the behavioral data was so reliable. Behavioral data is so powerful because if a person has done something before, they are more likely to do it again.

Enjoy the show!

We speak about:

[01:40] How Olivia started in the data space
[07:50] Data in the financial services industry
[08:50] Oliva’s career history
[13:35] Starting a consulting business
[17:10] Tips for explaining data science to non-technical people
[18:30] Becoming a published author
[24:45] Learning about Holacracy
[29:00] Balancing Holacracy and teamwork
[31:40] Combing data and human skills
[40:20] The Love@Work Method
[47:15] One of Oliva’s professional fails
[51:10] Using LEAP (love, energy, audacity, and proof)
[54:30] Following our intuitions

Resources:

Oliva’s Website: www.lovemakeityourbusiness.com

Data Science Consulting: www.oliviagroup.com

My Big ‘Why’ - https://tinyurl.com/LOVENEWCOMPETITIVEEDGE

LOVE@WORK now available at https://tinyurl.com/OLIVIAPRLOVEATWORK - A Silver Nautilus Book Award-Winner

The LOVE@WORK MethodTM now available at https://tinyurl.com/TheLOVE-WORKMethod

What is your Corporate Love Quotient? Find out here www.corporatelovequotient.com

Oliva’s Social Media:

Facebook: https://www.facebook.com/LoveMakeItYourBusiness/

LinkedIn: https://www.linkedin.com/in/oliviagroup/

Twitter handle: #OliviaParrRud

YouTube: www.OliviaOnYouTube.com

Instagram: Love.MakeItYourBusiness

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Prashant Natarajan has 18+ years’ experience in building EMRs, ERP, big data platforms, actionable analytics, and machine/deep learning applications. Before joining Deloitte, he served in hands-on global consulting and product leadership roles at H2O.ai, Oracle, McKesson Payer Solutions, Healthways, and Siemens. Prashant is Co-Faculty Instructor of Data Science and AI at Stanford University School of Medicine, Palo Alto, CA, USA. He volunteers as an industry expert and guest lecturer at leading Australian universities. Prashant serves as an industry advisor at the CIAPM computer vision project in University of California San Francisco, Council for Affordable Health Coverage, and Pistoia Alliance Center for Excellence in Artificial Intelligence.

In this episode, Prashant describes how essential human interaction is for success. In a technology-heavy space, human interaction and linguistics were not very common. Instead of complaining about it, Prashant went and got his masters to focus on English in the technology space. To have success, we need a clear understanding of culture. Culture is language, and language at its core is mathematics. How do we interact with people to figure out what their strengths are? Prashant considers himself the luckiest person on earth to have the experiences he has had in his career.

Enjoy the show!

We speak about:

[01:25] How Prashant started in the data space
[03:45] Studying communications and linguistics
[08:45] Mentoring young professionals
[11:45] Work with people who are smarter than you
[15:00] Merging business problems with data science
[19:45] The value business leaders see in data
[25:00] Advice for companies who are moving into data-driven products
[29:45] What excites Prashant about the future of data
[34:05] Horizontal capabilities
[37:20] The use of machine learning in healthcare
[44:20] Improving product development
[48:40] Prashant’s proudest moment
[50:15] The manufacturing industry
[52:20] We learn more from our failures than our successes

Resources:

Prashant’s LinkedIn: https://www.linkedin.com/in/natarpr/

Demystifying Big Data and Machine Learning for Healthcare (Himss Book)

Quotes:

“Human interaction is the most key determiner of success or not.”
“Today, we have the technology that has caught up with the human need.”
“Data science is increasingly a horizontal capability that will impact all of us.”
“I celebrate relationships because they allow me to learn.”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Demystifying Big Data and Machine Learning for Healthcare (Himss Book)

In this episode, Anthony Ugoni, one of Australia’s more prominent leaders in analytics interviews Felipe. Felipe came to Australia as a backpacker and ended up falling in love with the place. With Spanish as his first language, the only English he could say was the jacket is black. Then, Felipe explains some of his odd jobs and working freelance IT. At university, Felipe wanted to specialize in data, but all of his friends told him it was dead. So, he ended up specializing in hardware, even though all of his work was in data. When Felipe went to do his thesis, he happened to stumble into a project involving brain wave activity. The electrical engineer did all the research and design, the signals would be passed to Felipe’s computer, where he made his first application of machine learning.

Then, Felipe explains how he and a colleague of his made the decision to quit their jobs at a small consulting firm. They decided to start their own firm, despite knowing very little about business. The first year they almost went bankrupt about four times and made lots of mistakes. They wanted to be in analytics but were unsure how to sell their services. The two spent six months creating a piece of software. When they went to show prospects they found out people did not like the entire product. So they decided to focus on their consulting business.

Enjoy the show!

We speak about:

[02:40] Felipe’s background
[06:10] Education and specializations
[14:30] Quick delivery of value
[17:20] A series of odd jobs and IT freelancing
[24:20] Setting up his own consulting company
[33:15] Highs and lows of Clear Blue Water
[37:30] Executive Director & Head of Data Science at ANZ
[47:55] Supportive and open culture at work
[52:40] Understanding the business at a new job
[54:45] Inspiration behind Data Futurology
[62:00] Explainable AI

Resources:

Felipe’s LinkedIn: https://www.linkedin.com/in/felipefloresanalytics/?originalSubdomain=au

Episode #21 Antony Ugoni: https://www.datafuturology.com/podcast/21

Quotes:

“If I’m an engineer, people will think I’m smart.”
“A colleague of mine and I decided to set up our own consulting company. Professionally, it was the best and worst thing I’ve ever done.”
“Sales is built on trust and a human connection.”
“I had not done a good job of being a leader and creating a culture.”
“How can we make data scientists today, the CEOs of tomorrow?”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Dr. Gregory Hill leads the Analytics function at Brightstar's Global Services division, developing and delivering their data & analytics strategy, innovation programs, and product development initiatives. He works across their lines of business, including supply chain optimization, product portfolio management, financial services, buy-back and trade-in, leasing, and omnichannel solutions. He also manages Brightstar's analytics team in support of their key global accounts with pre-sales, solution design, and service delivery. His expertise is in the application of advanced analytics techniques (including machine learning, predictive modelling, mathematical optimization, econometrics, and operations research) to commercial problems. These applications span forecasting, pricing, fraud, market segmentation, customer satisfaction, and propensity modelling.

In this episode, Gregory explains how he started in the data space. He was aware of all the theoretical work being done around data but did not know how it worked in an industry aspect. The real challenge of putting mathematical models to practice lies in the organizational and people elements of it. Computer science and electrical engineering do not teach you how to overcome organizational challenges and individual motivations and incentives. Going back to get his Ph.D., Greg wanted to do something requiring qualitative research. So he targeted informational systems and economics. His fieldwork leads him to interview executives of larger banks, publicly listed companies, and government agencies. He came up with an economic framework that improved customer data quality.

Enjoy the show!

We speak about:

[02:00] How Greg started in the data space
[11:10] Leaving academics and getting involved in the industry
[13:20] Greg’s work background
[18:25] The four P’s of marketing
[20:40] Transiting from gut instinct to a data-driven approach
[27:55] Thinking through cause and effect
[30:45] What Greg’s team looks like
[39:00] Lessons learned from managing data scientists
[42:25] Active in local data science meetups + guest speaking
[44:25] Working globally + peeling back opportunities to use data science techniques

Resources:

Greg’s LinkedIn: https://www.linkedin.com/in/gregoryhill/?originalSubdomain=au

Brightstar: https://www.brightstar.com

Quotes:

“My thesis was not a project; it was a lifestyle.”
“I didn’t want to be an academic, I wanted to get back into the industry.”
“It was a combination of arrogance and laziness.”
“At the end of the day, it boils down to if I change X, will Y change?”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Sveta Freidman is a data scientist and business intelligence leader with extensive experience in consulting and client-based environments. She has a vast experience working in different industries, including gambling, retail, health, and online businesses (startups). Sveta is a data strategist with a passion for connecting people to the data they need to make decisions, build better products, and execute marketing strategies.

In this episode, Sveta explains why she decided to study statistics, she had a passion for mathematics. During her time in Israel’s military, she collected data from different places and made sense from it. Her commercial experience comes from various startups she joined. When joining a startup, you have to wear many hats. Sometimes you have to be a data engineer, data scientist, or a data analyst. Then, Sveta moved to Australia and found a startup, Envato, where she built all the data from scratch.

Enjoy the show!

We speak about:

[01:40] How Sveta started in the data space
[07:15] Sveta’s professional background
[17:10] Investing in local talent
[20:45] How to hire for a startup
[24:30] Questions for hiring interviews
[29:25] Working for Carsales
[31:55] People not trusting the data
[35:20] Solving the issue of trust
[40:30] Finding bias in the data
[44:50] Make sure you look at the data every day

Resources:

Sveta’s LinkedIn: https://www.linkedin.com/in/sveta-freidman-5981593

Carsales: https://www.carsales.com.au

Quotes:

“Statistics is everywhere.”
“You can be a great data scientist, but you need to understand the culture.”
“I give the candidate a business problem to see how they will react to it.”
“Your algorithms are good as long as your data is good.”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Vladimir Iglovikov graduated from university with a degree in theoretical physics, he moved to Silicon Valley in search of a data science role in the industry. This led him to his current position in Lyft’s autonomous vehicle division where he works on computer vision related applications. In the past few years, he has invested a lot of time in Machine Learning competitions leading to his title of Kaggle Grandmaster.

In this episode, Vladimir explains how difficult it was to find work in Silicon Valley. He had harsh requirements for a salary, no one looked at his resume. Companies in Silicon Valley are willing to pay big bucks, but at the same time, they require the person to be skilled in software engineering, machine learning, and statistics. His biggest issue when applying for jobs was assuming that all people are similar to the people in academics. At his interviews, he felt no connection with the interviewers. After sending his resume to over 200 different companies, someone finally bit just before his visa expired. Vladimir worked at Bidgely for 8 months then moved to TrueAccord and eventually got his job at Lyft.

Enjoy the show!

We speak about:

[02:00] How Vladimir started in the data space
[12:30] Transferring from academia to industry
[21:40] Benefits of having soft skills
[25:45] How Vladimir manages stress
[31:30] Kaggle is like lifting weights
[35:30] The hiring process for data scientists
[40:45] Excitement for machine learning
[46:00] Autonomous driving
[47:55] Pursuing a startup
[51:40] Aiming to maximize mistakes in a day
[61:00] Social life comes first

Resources:

Vladimir’s LinkedIn: https://www.linkedin.com/in/iglovikov/

Kaggle: https://www.kaggle.com/iglovikov

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016.

In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams.

Enjoy the show!

We speak about:

[01:20] How Tony got started in data
[08:20] Tony’s skills come from the commercial side
[11:10] Linking data science and the business
[14:30] Communicating how data science works
[17:00] Steps to getting others to understand data science
[20:40] Getting the best talent for your team
[24:00] Structuring teams and the department
[28:10] Transiting from analytical roles to commercial roles
[35:30] Working on global expansion
[38:10] Solving with artificial intelligence
[42:30] Passionate about using numbers to reach an outcome
[44:00] Modelling failures with Sportsbet
[47:50] Imposter syndrome in data science
[50:05] Data science is rapidly changing and exciting

Resources:

Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/

Sportsbet: https://www.sportsbet.com.au

Tony’s Twitter: https://twitter.com/gruebz?lang=en

Quotes:

“There is no one path that always works.”
“There are literally thousands of things data scientists couldn’t potentially tackle in any business.”
“If you’re not making mistakes, then you aren’t pushing the envelope hard enough.”
“Not having imposter syndrome is a sign of lack of knowledge.”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Today we have a different type of episode, this is a presentation that Felipe did at the Chief Data and Analytics Officer Conference in Canberra, and it is on explainable AI. First, Felipe explains how Amazon used a secret AI recruiting tool that had a bias against women. Also, the U.S. government used an algorithm predicting how likely people in the criminal justice system would reoffend. What they found is that it targeted specific racial groups. The algorithm isn’t racist or sexist, the data is. Regarding job applications, as your company scales up, the need to automate the process of looking at the applications becomes necessary. Sometimes, bias will creep into the automated decision-making algorithm. The bias can even be narrowed down to the person’s name. For example, somebody with name Felipe might get scored lower than somebody with the name Tyler. Lean into the inequality and predict the bias. You can plug in the CV information, and ask the algorithm to predict the person’s race and gender. Then, find out what key inputs they are flagging to determine this and remove them from the algorithm. Then, Felipe explains how algorithms can tackle unstructured data approaches. When discussing images, an algorithm was able to correctly identify a wolf from a husky 5 out of 6 times. However, when uncovering how the algorithm determined which was which, it was merely looking at if the animal was in the snow or not. If the picture had snow in it, then it must be a wolf. To determine how this algorithm was functioning, Felipe used LIME - Local Interpretable Model-Agnostic Explanations. It works for classifications and came out of a study from MIT. Later, Felipe discusses using EL15 and how transparency is essential for the public to understand how the algorithms could affect them. Enjoy the show!

We speak about:

[03:40] Large companies and their biases [05:40] Racism and sexism is in our data [08:45] Uncovering inputs of the bias [10:45] Unstructured data approaches [14:30] Using ELI5 [19:20] The right to an explanation

Quotes:

“We teach our algorithms on how to replicate our decisions.” “The algorithms show the inequality that we have in the world today.” “Explainable AI is more ethical in the sense that it is more transparent.” “Explainable AI helps us avoid blunders and informs us how the algorithm perceives the data.”

Thank you to our sponsors: UNSW Master of Data Science Online: studyonline.unsw.edu.au Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au Fyrebox - Make Your Own Quiz!

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Yuval is an Analytics and Data Science professional with extensive commercial and academic experience. His interests and goals are to be working on interesting and practical problems where there is a need to discover and act on meaningful patterns in data, through advanced analytics and data science. I'm the founder and co-organiser of two meetups: Data Science Melbourne and MelbURN, a user group for Melbourne-based users of the R statistical and data mining programming language.

In this episode, Yuval tells us about how both of his parents are statisticians and inspired him to fall in love with data science. Growing up, he used Pascal to build spaceship games, and it motivated his passion for programming. Eventually, Yuval went for his Ph.D. and focused on applying how animals learn and behave to robotics. Simulated and physical experiments were pretty basic because robotics were not as advanced as they are today. Later, Yuval realized academia was not necessarily his calling, he was more interested in applying solutions to interesting problems. However, in recent years, research innovation and solving problems are becoming much more intertwined.

Enjoy the show!

We talk about:

[01:40] How Yuval fell in love with data science
[05:45] Social learning in biology
[08:05] Lessons learned from completing a Ph.D.
[13:10] Research innovation vs. solving problems
[15:40] Embrace simplicity
[18:00] Small business advantages
[21:45] Skills to develop before management
[26:00] Results oriented work
[30:45] Different flavors of management
[32:50] Connection to community
[40:20] Learning to interact with stakeholders + managerial skills
[44:00] Benefits of building connections + education
[48:00] Assume people are at work with good intentions
[52:00] Allocate time for professional development
[59:30] Focus on retention

Resources:

Data Science Melbourne

MelbURN

Yuval’s LinkedIn

University of New South Wales

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Dr. Eric Daimler is an authority in Artificial Intelligence & Robotics with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. Daimler is the author of the forthcoming book Every Business is an AI Business, a guidebook for entrepreneurs, engineers, policymakers, and citizens on how to understand—and benefit from—the unfolding revolution in AI & Robotics. A frequent speaker, lecturer, and commentator, he works to empower communities and citizens to leverage AI & Robotics. For a more sustainable, secure, and prosperous future.

In this episode, Eric explains how he has a vivid memory of getting a computer at the age of nine. He loves the machine, and even at such a young age saw the freedom a computer allows. Early in his career, Eric knew he wanted to work with brilliant and motivated people. When he was in New York, he saw the Netscape browser and instantly recognized the world was going to change. This inspired him to get out and find opportunities on the west coast.

Enjoy the show!

We speak about:

[02:10] How Eric started in the technology space
[05:15] Moving from one career path to another
[09:50] Eric’s most significant failure as an investor
[13:30] Picking the timing
[18:15] AI is larger than what currently exists
[21:30] Embracing the technology behind AI
[29:45] Hurdles for companies who are adopting AI
[41:30] Reactions from people learning about AI
[48:40] Shortage of truck drivers + how technology is making driving easier
[54:00] AI in the medical field
[61:30] Using a categorical approach

Resources:

Eric’s LinkedIn: https://www.linkedin.com/in/ericdaimler/

Eric’s Twitter: https://twitter.com/ead

Website: http://conexus.ai/

Now you can support Data Futurology on Patreon!

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Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Martin Ford is a prominent futurist, New York Times bestselling author, and leading expert on artificial intelligence and robotics and their potential impact on the job market, economy and society. His 2015 book, "Rise of the Robots: Technology and the Threat of a Jobless Future" won the Financial Times and McKinsey Business Book of the Year Award and has been translated into more than 20 languages.

In this episode, Martin discusses his best-selling books and describes some of the themes he writes about. For instance, in Rise of the Robots he talks about “The Triple Revolution” which was a report presented to U.S. President Lyndon B. Johnson fifty years ago that argued this would be a dramatic change to the economy; however, it never really panned out. Martin’s argument for artificial intelligence started back in 2009 after writing his first book titled The Lights in the Tunnel. Ultimately, artificial intelligence will become so powerful that it can have a significant impact on employment that will compete with a large fraction of the workforce.

Enjoy the show!

We speak about:

[02:50] Martin’s background
[05:45] The themes behind Martin’s writing
[08:35] Machine learning is when algorithms can make decisions
[12:00] Amazon is susceptible to automation
[16:45] The most common occupation error is driving some kind of vehicle
[18:15] The type of work that will be left for humans
[21:45] Universal basic income
[28:55] Building explicit incentives to earn more income; paying people more to pursue education
[33:25] Artificial intelligence will be the primary force shaping our futures
[38:35] The solution is not to teach everyone how to code
[41:30] Architects of Intelligence: The truth about AI from the people building it
[46:00] Deep learning is the biggest thing to happen to artificial intelligence
[52:20] Controlling data and an entirely new industry called data banks
[53:15] Negative implications of artificial intelligence
[64:40] You do not want to be doing something predictable

Resources:

Martin’s Website: https://mfordfuture.com/about/

Martin’s LinkedIn: https://www.linkedin.com/in/martin-ford-5a70428/

Martin’s Twitter: https://twitter.com/MFordFuture

TED Talk: https://www.ted.com/talks/martin_ford

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Architects of Intelligence: The truth about AI from the people building it

Annie South is the General Manager of Data at ME Bank. She is an Information Management professional with twenty years’ experience of complex information environments spanning the full spectrum of structured data to unstructured information. Annie has in-depth technical knowledge of various specialisms, including metadata, data warehousing, data governance, data quality, enterprise architecture, data lineage, Big Data, data analytics, and regulatory requirements.

In this episode, Annie explains the things she does to ensure her career is future ready because nobody can predict what jobs will look like years from now. Do not specialize in a particular technology but specialize in a capability. The technologies that you are using today will not be the technologies they are using tomorrow. If you specialize in a particular technology set, and it is decreasing in popularity, you will end up with fewer opportunities in the market. Annie tells people wanting career advice that when people look at your resume, they are looking for a consistent arc. That could mean staying consistent in an industry or constant engagement in the workforce. Another thing Annie looks for in applicants is kindness, this quality is something that cannot be taught.

Enjoy the show!

We speak about:

[01:20] How Annie got into the world of data
[10:00] Insight for people starting in the data space
[12:50] Organizations are not predictable
[14:50] Annie’s team at ME Bank
[27:50] Turning recruitment on its head
[33:20] Transitioning from teaching to general manager
[39:05] Sort out your personality and experiment with leadership
[46:30] Imposter syndrome
[49:10] Experimenting with diversity in the workforce
[53:30] Challenges with discrimination in the workplace
[61:10] Define yourself; do not be defined by others

Resources:

Annie’s LinkedIn: https://www.linkedin.com/in/annesouth/

ME Bank: https://www.mebank.com.au

IT Jobs Watch: https://www.itjobswatch.co.uk

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Pavel Pleskov is a data scientist at Point API (NLP startup) and currently ranks number 3 out of 109,624 on Kaggle, making him a Grandmaster. Pavel has started companies in the past and has worked in many different industries before becoming a data scientist and Kaggle Grandmaster.

In this episode, Pavel explains his background and how he started in the data science space. When Pavel’s girlfriend went to pursue her master’s degree in London, Pavel began interviewing for a quantitative research job nearby. Turns out, the company was a rival of his current employer, causing him to get fired from his job the next day. Former employees of this job contacted Pavel to ask if they would join their new trading firm and be head of their research team. After doing his job for two years, Pavel knew he was capable of doing it on his own. The company works remotely, and after spending time in bitter Russian winters, Pavel looked to work elsewhere. The ideal country turned out to be Vietnam and was Pavel’s first time outside of Russia.

Enjoy the show!

We speak about:

[01:50] How Pavel started in the data space
[09:50] Vietnam is an ideal space for working remotely and teaching English
[21:40] The moment Pavel found Kaggle
[24:20] How Pavel became a data scientist
[28:00] Difference between machine learning engineers and researcher data scientists
[31:50] Why is it essential to be the very best?
[34:20] Machine learning and mathematics
[36:45] The early days of Pavel’s Kaggle journey
[40:00] Pavel’s favorite part of Kaggle
[47:20] The role of automation in Kaggle
[49:40] The steps when approaching a new Kaggle competition
[52:55] Think twice before you commit to data science

Resources:

Pavel’s LinkedIn: https://www.linkedin.com/in/ppleskov/?originalSubdomain=ru

Pavel’s Kaggle: https://www.kaggle.com/ppleskov

Pavel’s Twitter: https://twitter.com/ppleskov

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Danielle Timmins is the Chief Data Analytics Officer for Free Range Creatives. Free Range Creatives is a digital marketing agency that is deeply rooted in data and analytics. They have a different view on agency life and challenge the existing ways of working. They believe that work should be fun (well, at least most days) and that our work must be insightful, inspirational and effective. In this episode, Danielle tells us how she did not start in the data space but initially wanted to be a doctor. Danielle ended up getting a Master’s in Economic Psychology, during which she concentrated on the digital side of marketing. This is where Danielle got her exposure to data and started to understand it. Danielle got her first start at an NGO in a marketing position. She would shoot mini-documentaries for television and then moved into a more traditional marketing role. Danielle’s first job as a strategist was down in South Africa where she worked with several different clients. This is when she would start to work with data and incorporate it with strategy.

Enjoy the show!

We speak about: • [01:45] How Danielle started in the data space • [03:20] Background and career • [06:20] Deciding what problems to tackle first on the job • [08:35] Evolution of marketing • [13:35] Favorite failure • [16:50] How to communicate data • [18:30] Visual presentation style • [19:45] How Danielle creates a story • [21:30] How do you structure visuals for executives? • [23:10] How do you think people can get better at this skill? • [24:45] What is a strategist for data? • [27:40] What is the role outside of data? • [29:00] The main challenges for Danielle’s clients • [32:30] Working with clients on case-by-case basis • [33:30] Qualities of a great data scientist • [35:30] What do you think makes a good data leader? • [36:15] Current challenges in the data space • [37:40] Future challenges for the data space • [42:40] Advice for future data scientists and leaders

Resources: Sexy Little Numbers Free Range Creatives: https://www.freerangecreatives.co.za/ Danielle’s LinkedIn: https://www.linkedin.com/in/danielletimmins/

Now you can support Data Futurology on Patreon! https://www.patreon.com/datafuturology Thank you to our sponsors: UNSW Master of Data Science Online: studyonline.unsw.edu.au Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au Fyrebox - Make Your Own Quiz!

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Peter Elger is the founder and CEO of fourTheorem; his focus is on delivering business value to his clients through the application of cutting edge serverless cloud architectures and machine learning technology. His experience covers everything from architecting large-scale distributed software systems, to leading the internationally-based teams that built them.

In this episode, Peter tells us how his first real passion was in physics. After graduating with a BSc in Physics and a master’s degree in Computer Science, he worked for several years at the Joint European Torus (JET), the world's largest operational magnetically confined plasma physics / nuclear fusion experiment. They were doing big data, but at the time they did not refer to it as such; they dealt with around four to five terabytes of scientific data. Peter then transitioned to Indigo Stone as a Senior Technical Architect. Indigo Stone was a software disaster recovery firm which exited in 2007 to EMC.

Peter explains how it is essential to keep your technical skills up-to-date and why some of his favorite days are when he gets to code despite being the CEO of his company. If you can actually be the bridge between the business and the technology, you are an invaluable asset to any company. The freedom to innovate is what led Peter to his entrepreneurial ventures; previously, he had no real experience being his own boss. Peter says it is dangerous to think you can do everything; you have might a broad skill set, but you need to recognize that you have gaps. This is why Peter has always started businesses with co-founders. Currently, his co-founder is a world-class technologist and someone who understands the human dimension. All of his current co-founders and people Peter has worked with previously.

Enjoy the show!

We speak about:

[01:45] How Peter started in the data space
[06:50] Transition to disaster recovery
[08:55] Interactive radio and marketing applications
[13:40] Maintaining a grip with technical skills
[16:20] The entrepreneurial bug came organically to Peter
[18:40] Transition to entrepreneurship
[21:45] What to look for in a co-founder
[26:00] Building analytics with machine learning
[29:00] A tale of two technologies
[33:00] Applying AI to existing platforms
[35:10] Knowledge of AI is not necessary to use AI as a service
[37:50] Capable team members are difficult to find
[40:10] Sharing management meetings with all staff members
[44:05] Experiences with handling politics in organizations
[48:50] Removing ego + allowing the team to do their best work
[50:30] Scheduling work to maximize the impact

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Prakash Baskar is the Founder and President of Khyanafi. He helps data leaders to rapidly transition and accelerate the success of data, analytics, and digital initiatives. Previously, Prakash was the Chief Data Officer at Santander Consumer USA where he led enterprise data governance, risk infrastructure & information (risk data aggregation), data quality, business data strategy & solutions, and business & reporting analysis functions.

In this episode, Prakash tells us how he started in the data space at his university. His role was to determine how students were performing. If they are not performing well, he needed to identify why. The graduation rates were low at the school, so Prakash was tasked with finding out what was the problem. Then, Prakash discusses starting a new job and having little direction about what to do. With everchanging technology, the description of your job will always be changing too. As a person going into any role, understand that you do not have to ask permission all the time. Have a clear idea of what you can do and what you cannot do, then do what you feel is right for the organization. Look for where the opportunities for expansion are and find a way to get results.

If you ask ten people what the role of a Chief Data Officer is, you will get ten different answers. Whatever the CDO does will ultimately be to enable others to receive real benefits out of the data. Just because something is not broken, does not mean it cannot be improved. There are many different routes a person can take to become a CDO; however, you need someone with knowledge in multiple aspects of business, technology, and people management. A CDO needs to create value for the organization; learn the company you are supporting to anticipate the problems they may run into.

Later, Prakash explains how in business, any change is hard. How you embrace the change after it is made is what will differentiate yourself from others. If the change is too complicated, people will shut off. Start off by telling the client what the change will do for them rather than the steps it will take to get there. Some other tips when presenting a significant change is to be realistic with what it will take and make sure not to overpromise. It is imperative to select things that you can quickly do with minimal engagement from their people. Plus, make sure you have updates for the company each month, so they understand what is being revealed from the data. Finally, Prakash discusses how essential it is to move around the organization in order to understand different departments and he reveals the inspiration behind his latest business venture.

Enjoy the show!

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Valeriy Babushkin is the Head of Data Science at X5 Retail Group where he leads a team of 50+ people (4 departments: Machine Learning, Data Analysis, Computer Vision, R&D) and increases profit in a 25+ billion USD company. Also, Valeriy is a Kaggle competition master; ranking globally in the top 60.

In this episode, Valeriy explains his background and how he started in the data science field. At one point, he received an offer for a senior position at a bank; it was the largest privately owned bank at that time in Russia. Valeriy did not find out that he was doing machine learning until working on it for two years. What someone is doing right now could be pretty close to machine learning, and they don't even know. Then, Valeriy speaks on how trust is essential to the job of a data scientist; not only between you and your boss but between you and other departments. Trust will make your job easier when explaining the data, the results, and how reliable they are for the company. However, if there is an existing data science department in the company, you will not have to work as hard to earn the trust of others because it already exists. Sometimes when data scientists join a company, they think their job will just be to code all day. That is not always the case, you will have to talk to many people and often be a business analyst.

Enjoy the show!

We speak about:

[01:45] How Valeriy started in the data space
[06:10] Transiting to working at a bank
[11:30] Understanding the business process
[15:10] Gaining trust from clients
[20:20] Data scientists are business analysts
[24:10] Expectations from the job interview
[25:50] Starting data science teams
[31:40] The type of mindsets to look for in a team member
[37:30] Different teams complement each other
[40:20] Valeriy’s journey with Kaggle
[47:40] Ethical challenges in the industry
[51:20] Persistence is key

Resources:

Valeriy’s LinkedIn: https://www.linkedin.com/in/venheads/

Valeriy’s Kaggle: https://www.kaggle.com/venheads

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Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Jay Liu is the Chief Data Scientist at Digital-Dandelion specializing in helping insurance, and medical organizations innovate by integrating the latest in Artificial Intelligence (AI), machine learning and big data into their systems. Knowing the best way to learn is by putting your money where your mouth is, Digital-Dandelion launched an online brand and built a customer AI to promote it. There were numerous technical and modeling challenges that were overcome, but in the end, they sold all their stock within three months. They had proven to themselves that customer AI worked. Organizations can have great depth and breadth of customer data from their long-term relationships of selling high-value products and services.

In this episode, Jay explains how he found himself in advertising and started getting fat because of all the Michelin star restaurants his potential clients would treat him to. His data science career began with loyalty cards and being incredibility confident. When someone uses a loyalty card, the company is collecting data. They will know exactly what you purchased and how much you purchased of each item. The customer will be rewarded with monthly coupons. Jay was in charge of coming up with the coupons that were designed to make the customer spend more money in the store. Knowing at least one data programming language will leverage what you have and give you one foot in the door. The best way to get into data science is to know how it will improve the current industry or business you are working for.

Later, Jay explains why QA is a lost skill and the idea that great data scientists have internal discipline. However, there is a race to push the boundaries and become more automated. For example, Facebook collects as much data as possible and thinks about the consequences later. Data is data and people are people. Understanding data is the starting point. Before Jay starts a job, he dives deep and analyzes what every number means to the business with their data collection. Also, Jay considers how to make his bosses job as easy as possible. Overall, the success of his boss will create the most significant impact on his business. If someone has been working at the same job for ten years, they are scared to grow and try something new. Finding a data scientist who has worked at multiple different sizes and types of organizations is the key to finding a well-rounded employee.

Enjoy the show!

Resources:

Jay’s LinkedIn: https://uk.linkedin.com/in/jay-liu-76ab2b8a

Digital-Dandelion: https://www.digital-dandelion.com

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Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Marek Rucinski is the Deputy Commisioner leading the Smarter Data Program at the Australian Taxation Office (ATO). Marek has taken part and driven the evolution and transformation of Marketing, Analytics, Data and Digital capabilities for over 20 years. This has been done in both industry roles and consulting services capacity, across Australian, Asian and Global clients, across Retail, Telco, Consumer Goods, Financial Services, Mining & Utilities sectors. His passion centers on helping clients change the role of Marketing & Analytics capabilities in Digital and Data age, from activating the capability through acting on insights, to transforming customer experience and the whole business via delivering value across business functions. Prior to ATO & Accenture, Marek lead and created analytics functions and teams in a Retail industry, and developed global corporate strategy frameworks and analytics in a multinational organizations.

In this episode, Marek tells us about how he was always interested in the science behind marketing. Marketing as a discipline has been completely transformed due to the emergence of data as a driver for engagement with the customer. Marek is not a classically trained data scientist; he is a data strategist and can dive deep into the organization’s needs in order to drive value to the customer. Marek tells us how some businesses can struggle with how to handle the findings of research from data scientists. It is essential to translate the potential into targets to create the prize. Leave the ego at the door and find the ability to be critiqued.

Later, Marek tells us how educating businesses on analytics as a mechanical process is essential for them to perceive how the whole thing works. He then explains his transition from consulting to government and how his excitement lies in the play with analytics at an enormous scale. Then, Marek describes how to have each section of the value chain working with purpose and precision. Data has to be trusted, organized, and accessible for the company. A data strategist must consider how the data is being delivered to their client. You want to create products and interactive experiences for the business as opposed to simple spreadsheets. Finally, Marek answers the audience’s questions including what makes a good data scientist and current challenges in the data science industry.

Resources:

Marek’s LinkedIn: https://www.linkedin.com/in/rucinskimarek/

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Jonny Bentwood is the Global Head of Data & Analytics at Golin. Jonny is an innovative leader with 15+ years of experience in communications - winning, retaining and working for Fortune 100 clients such as Facebook, Unilever, Heineken, Barclays, HP and Microsoft. He has a proven record as a creator of pioneering solutions with ability to transform business to radically impact bottom line. Jonny presents complex information in an engaging and informative style and is a strategic consultant to executives using data to provide guidance on reputational and crisis issues and maximising marketing campaigns.

In this episode, Jonny tells a story about how MTV got in touch with him to apply data in figuring out who would most likely win The Apprentice. After being in the industry for over twenty years, he believes this is the best time to be in data. CMOS are spending more of their money than ever before on analytics. How do data scientist prove their value? People use data purely in a descriptive way. To succeed and bring value to clients, one needs to switch from describing the data to telling the customer what they need to do with the data. Set the goals of who, what, and why to figure out which message will be most useful before you even start. Take it a step further by using prescriptive data and make it predictive. This is where you study what will happen in the future. We are continually absorbing and understanding what things could happen and will happen. This opportunity is essential to identify issues before they occur and fix them.

We speak about:

[01:30] How Jonny started in the data space
[04:50] Public relations
[06:00] Descriptive, prescriptive, and predictive
[08:15] Difference between interesting and useful
[10:00] Understanding the customer
[15:25] Cultural shift of data in organizations
[19:10] Challenging the status quo
[22:40] Shiny object syndrome
[26:45] The twenty percent time
[30:00] Bringing data application to the masses
[34:30] Each stage of the customer journey
[39:30] Getting value for money
[42:45] Return on investment
[44:15] Data + creativity

Resources:

Jonny’s LinkedIn: https://uk.linkedin.com/in/jonnybentwood

Jonny’s Twitter https://twitter.com/jonnybentwood?lang=en

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Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Warwick Graco is the Senior Director of Data Science at the Australian Taxation Office (ATO). He has worked in defence, health, and taxation and has been involved in analytics for 25 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a Ph.D. from the University of New England Australia. His professional interests include organisational innovation and learning, organisational decision making and analytics.

In this episode, Warwick tells us how he got started in data research the skills gained that led him to his successes today. Warwick explains why transparency is a business requirement for software and tools in the data science field. People with more analytical backgrounds will be more willing to accept an opaque solution over a transparent solution. When analytics was in the early stages, some organisations pushed back from data science; feeling they were on top of their portfolio and did not need any outside resources. No matter what results Warwick would come up with for these organisations, they would continue to have the same attitudes. Since 2010, there has been a shift in attitudes because data science has shifted from the background to the foreground.

Then, Warwick tells us the difference between good support and lousy support in the workplace. While Warwick was working with organisations, instead of providing results, he did the reverse. Ask the organisation what they want rather than telling them the findings. Providing the outputs clients wish to see led to incremental improvements built into their business intelligence reports. Warwick also explains why you can no longer be a data scientist; you will need to learn and master the domain of your work. For instance, Warwick learned everything about ophthalmology while working on data science with an ophthalmologist. Later, Warwick explains his process of publishing research, improving privacy concerns, and automated supports.

Enjoy the show!

Show Notes:

• [02:20] How Warwick started in data science

• [05:55] Aptitude for research

• [08:40] Purpose-built software + decision trees

• [12:20] Accepting opaque solutions vs. transparent solutions

• [16:45] Pushback of data analytics

• [21:15] Difference between good support and bad support on the job

• [25:25] Necessity to learn the domain first

• [29:00] How to learn on the job

• [32:20] Process of publishing research

• [41:50] Improving legal and privacy concerns

• [44:25] Automated support + decision-making operations

• [52:40] Developing an analytical + practical mindset

• [58:10] Hyperspecialized

• [64:30] Moving toward data + analytics as a service

• [66:25] Advice from Warwick

Now you can support Data Futurology on Patreon!

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Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Caroline Worboys is a data expert, investor, advisor, COO at Outra & Vice Chair at DMA Group. She has been working in the data industry for over 30 years. In this time, she’s had a fascinating journey. She has worked, created, mentored and consulted through many data driven organisations. She’s played all the different roles: technical lead, a business lead, a founder and investor.

While Caroline doesn’t describe herself as a data scientist and didn’t go to university, she has always worked with data and has a wealth of experience. She started in the field by working with consumer data for direct marketing and progressed to the point where she founded and sold several successful data related start-ups. Currently, she is the founder and COO of Outra.

In this episode, we talk about what it was like being a woman in technology in the 80’s, how the use of data has progressed over the years and how she keeps her team focused on the goal of doing things faster than other companies.

Summary

How Caroline got started in data (03:02)
What she learnt from observing senior colleagues and what it was like being a woman in technology in the 80s (05:38)
Using customer data in order to target people at the right time (07:46)
The principles of working with consumer data hasn’t changed (10:04)
How the care and attention required for direct mail has now been lost with email and digital marketing (11:09)
The importance of being curious and learning (12:31)
Starting her own business and finding a different way to charge customers (13:46)
Advice for young people and why it’s important to seek people for advice (21:34)
Personal drivers to start her business (23:35)
How her business innovated as technology changed (25:10)
The challenge of using data to actually solve problems (30:29)
Considerations when choosing her team (35:48)
The recruitment process is like for Caroline’s company (39:00)
How Caroline keeps her team focused on the goal of doing things faster than other companies (41:40)
The difficulties of work/ life balance (44:16)
Considerations for being a leader in the data space (47:03)
The importance of thinking about the type of data you want to work with (51:43)

Quotes

“Seek out people who have really, honestly read the book and seen the movie and been there. Because they can stop you from going down a whole bunch of dead ends.”
“You can’t scale and have thousands of relationships with thousands of people. But you can create a culture, and processes below that culture, that are scalable.”

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Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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Kevin Harrison is working as Chief Data Officer and Deputy Chief Information Officer for the City of Oakland in California. Prior to this he worked as the first ever Chief Data Officer for the State of Illinois. During that time he designed the blueprint for the State Data Practice. Operating under the new Department of Innovation and Technology agency, he implemented an enterprise approach to Business Intelligence and Data Analytics, covering all 60 State Agencies to create a collaborative and sharing environment across the state.

Having worked with multiple organisations, Kevin has been able to handle different types of challenges in our industry. In today’s episode, Kevin shares the strategies he applied to move from smaller projects to bigger ones. How he has been able to help organisations increase their market share and improve operations. Kevin also shares why he thinks changing the perception of organisations about data and educating them about tools in the space is so important. He further talks about data governance and possible changes in role of the data scientist role in future.

In This Episode:

01:55 Professional background of Kevin

06:30 Why data is important?

07:20 Evolution of Data warehousing

10:00 How organizations are utilizing the data?

11:39 As data officer, how to help organizations to improve their data capabilities?

13:00 Building trust is crucial for project success

13:30 Transition from small to bigger project

16:12 Challenges faced as data consultant

19:00 Educating about the change coming to data science

21:00 Process of data strategy for organizations

23:50 Why so many data warehousing failed?

26:00 Importance of data governance

27:10 Biggest problem in data governance

31:56 Role of data storage

35:15 Challenges faced from moving to another industry/sector

38:42 Qualities data scientist should have

41:43 Future of data science

42:30 Advice to the listeners

Now you can support Data Futurology on Patreon!

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Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Mike serves as Head of Data Science at Uber ATG and lecturer for UC Berkeley iSchool Data Science master’s program. Mike has led several teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize he oversaw all data science product development and created the MS in Data Science program in partnership with UNH. Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena.

In this episode, Michael talks about how he accidentally got into data and his work with simulation. Then, Michael discusses his background in data science product development and data science education. He reveals all the mistakes he made with his transition from academics to industry. Later, Michael tells us what attracted him to data science education and how he balances industry projects with his teachings. Rapid growth is a challenge with technology management because your skillset will get rusty as the technology advances. Lastly, Michael talks fake news, bootstrapping, and Fake or Fact.

In This Episode:

[00:20] Michael accidentally got into data

[02:15] About Michael Tamir

[03:40] Transition to industry

[06:40] Software engineering challenges

[08:45] Data Science Education

[15:15] Adaptive learning

[17:15] Team management

[19:05] Challenges with rapid growth

[24:25] Fake news

[27:25] Toughest challenge

[28:50] Fake or Fact

[31:20] Listener questions

Mike's quotes from the episode:

“You have to be really careful about what you do and what you do not teach in order to make sure students are successful in the long-term.”

“Decisions are going to be best made by those who are closest to the ground.”

“You’re not going to be the expert in every group you are managing.”

“I take full responsibility for any failures with the algorithm.”

“Most of my time is spent on my day job.”

“Find out what you enjoy about data science skills; find the role that is looking for those skills.”

“I enjoy the science and making sure we are asking the questions in a scientifically sound way.”

Connect:

Twitter - https://twitter.com/MikeTamir

LinkedIn – https://www.linkedin.com/in/miketamir/

Website - http://www.fakeorfact.org

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UNSW Master of Data Science Online: studyonline.unsw.edu.au

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David Niemi is Vice President of Measurement and Evaluation at Kaplan, Inc., where he oversees efforts to improve the quality of measurement across all education units, evaluate the effectiveness of curricula and instruction, and study the impact of innovative products and strategies.

Previously he was Vice President Evaluation and Research, at K12 Inc., where he directed assessment development and validation, evaluation of products and services, and research studies used to drive curriculum development. He has been a co-principal investigator for a number of large-scale assessment research projects funded by the U.S. Department of Education and the National Science Foundation and has collaborated on Department of Defence training studies. As a researcher and professor at UCLA and the University of Missouri, respectively, he has also managed assessment research and development studies in school districts across the U.S. and has trained thousands of teachers and other professionals to design and use assessments more effectively.

David's new book is:

Learning Analytics in Education: Experts Explain How To Use Data To Understand and Increase Learner Success

New technologies, better measures and more data, all related to learning, hold the promise of helping educators increase their students’ success. The relatively new field of learning analytics has developed to help educators understand and use the increasing amounts of evidence from learners’ experiences. How can educators harness access to greater data to improve learning on a large scale?

Learning Analytics in Education is a new book written by a broad range of experts who explain their methods, describe examples, and point out new underpinnings for the field. The collected essays show how learning analytics can improve the chances of success for all learners through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings.

The collection was edited by four noted educational experts including David Niemi, vice president of measurement and evaluation at Kaplan, Inc., the global educational services company well-known for using advanced learning science and learning engineering methods in its programs and products.

"At Kaplan, we've been invested in using learning science and data analytics for several years to help us design courses and refine instructional methods to help students achieve better outcomes," explains Niemi. "Educators today face accelerating change as education undergoes a fundamental transformation driven by the replacement of traditional analog tools by digital systems and expansive data inputs." He adds, "Understanding how to use these new streams of available data to best guide student learning is the essential point of the book."

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Learning Analytics in Education

Kjersten Moody joined State Farm in July 2017 as Vice President and Chief Data & Analytics Officer in Bloomington, Illinois.

Previously, Kjersten led Data & Analytics and IT groups at global companies, such as FICO (Braun), Thomson Reuters and Unilever. She has a record of delivering tangible, positive business results, and a depth of experience in scaling operations, planning/executing mission-critical business initiatives, and achieving profitability objectives.

Kjersten is a graduate of the University of Chicago and has a proven track record in modernizing and scaling operations, executing mission-critical business initiatives, and achieving profitability objectives. An energetic leader with a focus on people development, diversity, and inclusion Kjersten demonstrates the ability to effectively lead and work in highly complex environments.

In this episode, Kjersten talks about her love for data and how it compliments an understanding of human behavior. She is incredibly grateful for the chances others took on her to get her in the role she is today. Understanding how to thrive in stressful situations is one of the essential lessons Kjersten learned in her early roles.

Her leadership style is open, honest, and collaborative while always ensuring to take time out of her day to serve others. In the healthcare industry, Kjersten gets to see her work through and enjoys the process of continuous improvement. Building teams have not changed much, some methods of work differ and where the work is performed. For example, information security has grown significantly to evolve with the ever-changing advancements in technology. Later, Kjersten explains how she builds a team, what diversity means, data strategy, data governance, and financial impacts.

In This Episode:

• [00:20] About Kjersten Moody

• [04:45] Love for data

• [06:40] Transition to technology consulting

• [09:50] Lessons learned early on

• [13:15] Leadership took the time

• [14:40] Kjersten’s leadership style

• [15:35] Transition to healthcare

• [18:00] Lessons learned in consulting

• [20:00] Building teams

• [22:15] Qualifications for individuals

• [29:10] Data strategy

• [33:00] Data governance

• [38:00] Understanding the business aspects

• [45:20] Financial impacts

• [48:20] Listener questions

Some of Kjersten's quotes from the episode:

“Challenges are a constant in a domain such as data science.”
“Diversity is an attribute of the team. It’s the diversity of experiences, culture, and thought.”
“The process of matching price to risk is inherently done through data.”
“Data strategy is interpreted in many different ways.”
“The leader needs to be able to work in a trusted way with business leaders and general managers.”

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In this episode I talk to Matt Kuperholz. Matt currently works for PWC as a Partner in their Analytic Intelligence Area and is their Chief Data scientist. As a kid, Matt was fascinated by computers and while training to be an actuary started developing his computer science skills. This led to working as a data scientist and consulting with top tier companies.

In this episode Matt and I talk about his career journey, why it’s important to focus on the real world and not just the data and how data science can be integrated into businesses. We discuss the concept of responsible AI and why the exponential growth of technology is making for an interesting world.

With a background in both actuary and computer science, Matt has been working with data for over 20 years. He ran his own company in the early 2000s which included working with Deloitte Australia as they started to look at how to use data science in their business. He is now a is a partner and chief data scientist at PWC Australia. An expert in planning, executing and communicating the results of advanced analytics projects, Matt’s area of specialisation is the application of artificial intelligence and machine learning technologies to detailed and complex data.

Summary

· Matt’s love for computers and he he got to where he is now (00:12)

· How Matt’s interest in computers led to a love for data (06:28)

· Matt’s interest in martial arts and why a diversity of people matters (08:19)

· Smell-testing the quality of a number, and the importance of attention to detail (09:40)

· Working with limited time on a mainframe and how Matt coped with limited resources (12:09)

· The early days of using AI and what it was like working in a start-up in the late 90s (15:04)

· The importance of well prepared data (16:56)

· How Matt keeps up to date with data and technology (21:17)

· How Matt chooses what problems to tackle (23:26)

· What it was like working with Deloitte (26:03)

· How data can integrate into other areas of a business (28:32)

· Starting with the real world problem before focusing on the data (30:26)

· A recent project Matt has worked on exploring what trust looks like in a digital world (35:11)

· The idea of responsible AI and how we develop checks and regulation (41:41)

· How technologies are growing exponentially and causing a fast changing world (49:45)

· How Matt follows his curiosity and how this has led to opportunities (52:05)

· Why the data industry is worth getting into (54:48)

· The importance of finding what you are into and staying true to yourself (55:53)

Connect:

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Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

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In this episode, I talk about data scientists and ways you can attract the best talent to your team. Instead of telling your employees what they can do better, make them curious as to what they could do better. Then, I reveal the three things to look for when analyzing your pool of applicants. Once you have your team, now what? Once you have a decent pay settled, I explain the three things you will need to have for a capable team. Later, I tell you the elements, as a manager, you should be doing as rarely as possible.

In This Episode:

• [02:45] How to attract data scientists to your team?

• [04:45] The three things to look for from your pool of applicants

• [07:05] Adversity; test how they would react

• [11:00] Three things needed to run an effective team

• [18:00] Managers should be doing this as rarely as possible

Creating a Data Team Session Quotes:

“Create a learning environment and continually challenging projects to focus on their development.”
“People should be open-minded and willing to learn; I test this in two different ways.”
“A lot of people come with technical skills from other countries.”
“They had to code it live with about eight people watching them, no pressure!”
“You know the answer, and you want to tell them to get to the outcome quickly. That’s an urge you have to roll back and fight against.”
“Purpose is really what gets us out of bed every day.”
“Make yourself redundant as quickly as possible.”

Resources Mentioned:

Drive: The Surprising Truth About What Motivates Us

Connect:

Twitter - https://twitter.com/datafuturology

Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

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JCU Master of Data Science - Online Program

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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Drive: The Surprising Truth About What Motivates Us

In this episode I talk to Kristen Sosulski who is the Data Visualization Professor at NYU Stern School of Business. She has just written the book Data Visualization Made Simple: Insights Into Becoming Visual. An interest in using technology to help students learn has led to helping people to understand how to use data visualizations to communicate insights to others.

Kristen and I discuss guidelines on creating data visualizations, why presenting data visualizations is as important as creating them, and how the software needs to improve.

Dr Kristen Sosulski is an Associate Professor of Information Systems at New York University’s Stern School of Business. She teaches MBA, undergraduate, executive, and online courses in data visualization and computer programming. She is also the Director of the Learning Science Lab for the NYU Stern where she leads teams in design immersive learning environments for professional business school education.

Summary

• Kristen’s journey from doing her undergraduate in Information Systems at NYU Stern School of Business to being a professor there teaching Data Visualization (00:17)

• How Kristen’s love of technology led to an interest in using technology to help students learn (01:38)

• The challenges of trying to create an immersive learning environment in the late 90s (02:41)

• What led to Kristen working with data visualization (03:38)

• How Kristen thinks about data visualization and designing data graphics (06:14)

• Some guidelines and thoughts on presenting data to an audience (08:03)

• How people learn to improve their data graphics (11:15)

• The importance of showing your work and getting feedback (14:18)

• The challenges Kristen finds when consulting for companies in data visualisation (17:08)

• The value of data visualization in a data driven organisation (19:54)

• Why Kristen wrote her book on data visualization and why she included case studies (21:14)

• Some resources that Kristen created for the book (23:40)

• Her work in building NYU’s online education and the use of learning analytics (27:11)

• Why there needs to be more training in how to visualize data and to understand what it means (30:10)

• Designing a dashboard for user driven storytelling (33:41)

• How Kristen would like data visualization to evolve in the future (36:44)

• Mistakes people make when creating visualizations (38:51)

• How Kristen developed and improves her work and the value of sharing your mistakes (41:33)

• The importance of understanding what your data means in the real world (42:49)

Links

Data Visualization Made Simple: Insights into Becoming Visual by Kristen Sosulski

https://www.amazon.com/Data-Visualization-Made-Simple-Insights/dp1138503916

The Online Certificate in Visualizing Data

Taught by Kristen Sosulski via NYU Stern School of Business

https://www.stern.nyu.edu/programs-admissions/online-certificate-courses/visualizing-data

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A lot of listeners have asked what have been my takeaways from the 30+ discussions with the guests on this podcast so far. To launch 2019 I’ve done a look back at all episodes from 2018. This is part 2 where I discuss episodes 19 to 34.

I hope you enjoy my recollection of these conversations. I’d love to hear what were your favourite takeaways!

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A lot of listeners have asked what have been my takeaway points from the 30+ discussions with the guests on this podcast so far. To launch 2019 I’ve done a look back at all episodes from 2018. This is part 1 where I discuss episodes 1 to 18.

I hope you enjoy my recollection of these conversations. I’d love to hear what were your favourite takeaways!

Support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

Thank you to our sponsors:

JCU Master of Data Science - Online Program

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And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

Send in a voice message: https://anchor.fm/datafuturology/message

Sally is the General Manager of Insights at the Australian Motoring Services. She previously spent 10 years working in banking and today she shares her story.

We speak about: * Fraud analytics in big banks * End to end analytics * Importance of fast feedback loops * Shocks of early working life * Balancing speed & accuracy * 80/20 vs 95/5 * Exposures in strategy & politics * Helping the business ask the right questions * Leading with the work * Career breaks: how to * Importance of working on yourself * Advantages of medium sized companies * Creating a data strategy * Balancing tactical solutions, strategic initiatives and team development * Self service analytics * Educating business stakeholders & getting their feedback * Ability to ask anything from everyone * Data science is like medicine * Leveraging multiple dimensions for career development * Knowledge sharing sessions * Getting analytics a seat at the table

Show notes: www.datafuturology.com/podcast/34

Sally is based in Melbourne, Australia

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Graeme started in actuarial science and developed a love for algorithms and automation. He worked in data warehousing before moving into data analytics. He spent 16 years in several Head of Data roles at The Automobile Association (AA) before joining Addison Lee as their Chief Data Officer, where he is today.

We speak about: * What is actuarial science * Data warehousing & GIS systems * Overview of the Chief Data Officer role * Automation in the data space * How to build a data warehouse * The difference between a data warehouse, data lake and virtual data warehouse * Starting data work with business problems/questions * How to deliver value to the business * Balancing tactical project delivery with strategic work * Enabling self service data analytics * Prioritising & sizing up work * Modern styles of work in data * Data governance: creating a plan * Creating a data strategy * How to get to a head of role * Team building * Networking

Show notes: www.datafuturology.com/podcast/33

Graeme is based in London, Greater London, United Kingdom

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Carole had an unusual path into data science. She's worked as a content project manager, in strategic planning and in sales before getting into data through Business Intelligence at Fyber where she eventually became their Head of Analytics. Today she is the Head of Data Science & Analytics at Tenjin.

We speak about: * The strengths of being a generalist * Upskilling throughout your career * Focus on self service reporting * The skills needed in a BI team * Creating internal user groups to share knowledge * Convincing people to get training on the tools required to do their job better * The benefits of gaining a reputation internally * Setting a strategy for data teams * The importance of data modelling skills in data teams * Learning technology on the job when you're background is not technology * Monthly meeting with key departments to review all dashboards in the department * Working remotely in global companies * Metrics about user behaviour * Offering analytics for many customers with the same problem/need * How to develop consulting skills * The platinum rule - book on communication style * The leadership challenge - book recommendation * What it's like working in startups * How to recover from being a workaholic

Show notes: www.datafuturology.com/podcast/32

Carole is based in Berlin Area, Germany

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Scott started his career pushing trolleys at Woolworths. In his career he rose to management levels in retail with Woolworths, consumer goods with Kraft Foods, Fonterra SPC and PZ Cussons, then in media with 21st Century Fox. He then became the CEO of iSelect, a role he left earlier this year to start his own AI company Wilson AI.

We speak about: * Focus on customer needs * Digitising industries to access more data * Helping companies in multiple industries to begin their data analytics journey * How to differentiate your company when competitors have access to the same data * How to overcome being "data rich but insight poor" * Changing industry power dynamics through data * Creating new teams to create value from data * The importance of storytelling in data science * Defining objectives with your data analytics communication * Educating industries to use data more effectively * Understanding costs & priorities across the value chain to make better decisions * Eliminating your biases when dealing with customers * Process re-engineering & AI * How to think outside of the building * How to start an AI company * The importance of translating between business and technical * How to connect data science and the boardroom * The importance of data science education in an organisations journey * How to achieve a wider spread adoption of AI * Focusing on cost & revenue with data science for maximum impact * Resist the urge to boil the ocean * The role of a CEO in a publicly listed company * Focusing on the top 3 business priorities * Productionising AI & monitoring unintended consequences

Show notes: www.datafuturology.com/podcast/31

Scott is based in Sandringham, Victoria, Australia

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Aaron started his career working in accounting and building management information systems (MIS). He had his own company, worked in multiple industries and then got into biology and genomics. Today he is the Chief Data Officer at the Inova Translational Medicine Institute.

We speak about: * How to take research into scaled applications * The importance of sharing your knowledge and helping others understand * Why you're only as good as your team members * How to engage many different types of stakeholders * Challenges of data management in healthcare * Data governance & provenance in healthcare * Data monetization & it's stigma in healthcare * The benefits of data sharing consortiums * The potential of genomic & DNA data * Handling algorithm biases * Enabling reproducible research through data * Why "perfection is the enemy of good" * The importance of creating & sharing your mental models

Show notes: www.datafuturology.com/podcast/30

Resources:

Weapons of Math Destruction https://weaponsofmathdestructionbook.com

Evernote https://evernote.com

Real time board https://realtimeboard.com

Mind jet - mind mapping https://www.mindjet.com

Aaron is based in Washington DC Metro Area, USA

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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Klaus started his career doing internships at Yahoo! and the port of Hamburg. He worked as a consultant and completed a PhD in Quantitative Marketing. Today he is the Chief Analytics Officer at YAS.life

We speak about: * The importance of getting applied experience as early as possible * Defining KPIs for businesses * Using data to change organisational behaviour and increase safety * How to navigate organisations to create data definitions * Realities of consulting: positives and negatives * Why large companies require so much custom work * How to help people and organisations that don't know what they want * Helping organisations in progressing through their analytics journey * How to overcome technical challenges with creative solutions in your projects * Why honesty within yourself and others is imperative in your work * How to provide customers what they need instead of what they want * The importance of hard and soft metrics when measuring value * Applying soft skills in data science * How to find what will be valuable for your customers * Expanding your interest with a postgraduate degree * How your social surroundings affect your purchase decisions * Using soft skills for data acquisition * What is eigenvector centrality and what is it used for? * How product reviews influence your buying decisions * How to create experiments in business * Pricing models in the steel business * Data science in fitness startups

Show notes: www.datafuturology.com/podcast/29

Klaus is based in the Berlin Area, Germany.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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Jennifer started her career as a particle physicist before becoming a data scientist. After gaining experience in many fields including high frequency algorithmic trading & advertising, she was Atlassian's first Chief Data Scientist. Today she is the VP of Machine Learning at Figure Eight and an Expert and Advisor at the International Institute for Analytics.

We speak about:

How to see the results of your work sooner and faster
The importance of choosing your manager
Making data strategy decisions for companies that are very immature in their approach to data
Building data science teams from scratch
Combining impostor syndrome and leaps of faith for your benefit
The importance of making mistakes to be successful
What having a great data culture really means
How to convince peers and supervisors on the benefits and the path of data strategy
Differences between having a technical and non-technical manager
Combining technical abilities and business sense
The importance of customer contact for technical people
Focus on the impact and outcome of everything that you're building
How to keep the balance in teams
Pleasing customers vs product intuition
How to drive and create a data driven culture
How to create scale with your data science efforts
How to build your data science team
Data engineering vs Machine learning engineer
How to keep talent
How can data scientists learn the skills for business leadership
Active learning and building products for data scientists

Show notes: www.datafuturology.com/podcast/28

Jennifer is based in Mountain View, California

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Mark used to be a statistics lecturer at Nelson Mandela University in South Africa. He then joined First National Bank as a quantitative analyst where he climbed through the ranks to Head of Advanced Analytics and beyond. Today he is the Chief Analytics Officer at FNB.

We speak about: * Predicting what the customer is calling about * Improving compliance in banking through analytics * Creating and driving a data strategy across an organisation * Using analytics to look after customers in better ways * How to create and measure economic value from data * How to find meaning in your work * Understanding your value across the entire value chain * Creating a culture of collaboration that's not afraid to fail * Working with tertiary institutions to identify talent * What to test when interviewing data scientists * How to structure your team & work with stakeholders * The importance of data governance * How to implement and socialise the solutions created by the team for maximum impact * The importance of mentoring and growing people * The difference between head of analytics and chief analytical officer

Show notes: www.datafuturology.com/podcast/27

Mark is based in the Johannesburg Area, South Africa

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Sam's background is in sports & exercise science. He has an accomplished career in sport analytics. Today, he is the Head of Research and Innovation at the Western Bulldogs and an Associate Professor at Victoria University.

We speak about: * Using ML to help people see the non-linerarity in their problems * Common misconceptions of ML * Interpretability of ML * Using ML to improve athletes performance, measure their contribution & prevent injuries * Carving a data science job in an area you're interested in * How to choose projects to focus on * Mixing psychology, operations and data science in sport * Data collection & management in sport * How data can help off field & the mental side of the athletes * Similarities of data in sport and government/ corporate * How athletes change when fatigued * Applications of sports analytics * How data can help create drills to improve player performance & skills * Current modelling challenges in sport * Real time decision making in game by coaches: challenges and realities * Educating stakeholders

Show notes: www.datafuturology.com/podcast/26

Sam is based in Melbourne, Australia

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Ben started his career as a chemical engineer. He developed an interest for computer vision early on. He worked for Intel, then at a hedge fund and then became the Chief Data Scientist at HireVue. A couple of years ago he started his own AI startup called Ziff.ai where he's is building a Deep Learning platform for product visionaries and software engineers.

We speak about:

How computers amplify us
What it looks like to start your own AI company
How to switch programming languages
Downsides of Google's tensorflow
What industry expects from data science
How to deliver value with ML
How to pick ML projects to tackle
Eliminating bias in AI applications
AI powered job interviews of the (near) future
Topic discovery with DL
AI warfare in business
What is a Hive Mind and how it works
Future health care assessments at home
AI is cute until it's scary
The importance of passion and obsession in data science

Show notes: www.datafuturology.com/podcast/25a

Articles by Ben on Linkedin:

This is Why Your Data Scientist Sucks: https://www.linkedin.com/pulse/why-your-data-scientist-sucks-benjamin

The Al War Machine: Our Darkest Day https://www.linkedin.com/pulse/ai-war-machine-our-darkest-day-ben-taylor-deeplearning-/

The Al War Machine: The Hive Mind https://www.linkedin.com/pulse/ai-war-machine-hive-mind-ben-taylor-deeplearning-

Getting That Data Science Job https://www.linkedin.com/pulse/getting-data-science-job-ben-taylor-deeplearning-/

From 0 to $100K+ data science job in 6 months https://www.linkedin.com/pulse/from-0-100k-data-science-job-6-months-ben-taylor-ai-hacker/

Ben is based in the Provo, Utah Area

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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This is a different type of episode! This episode is a presentation I recently did at a large financial services institution. I presented on 5 Mistakes and Lessons Learned in Driving Business Value with Data Science and the Cloud.

I talk about: - Using Lean Startup and Design Thinking principles in Data Science - The importance of staying close to your end customer and what that looks like in practice - The difference between machine learning for machines and for humans - What is the purpose of ML/AI and how you can bring that thinking into your organisation - What using ML for humans looks like - Using data from other areas - Leverage the flexibility of the cloud

Show notes: www.datafuturology.com/podcast/24

Slides: http://bit.ly/df-5mistakes

Felipe is based in Melbourne, Australia

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Mario is an Electrical Engineer from Colombia. He went to Silicon Valley to do his Masters at Stanford University and stayed to build a career in Marketing Analytics. He has incredible experience and has worked at Intuit, Google, HP, Symantec and Facebook. He currently works at Uber as Marketing Analytics and Data Science Manager.

We speak about:

Starting in marketing analytics without knowing anything about it
Data dictators and why multiple versions of the truth are necessary
The importance of data science education in organisations
How to pick the best predictive model for your applications
How to use people analytics - Google style
Why your job is to empower your stakeholders
How to stand out during interviews

Show notes: www.datafuturology.com/podcast/23

Mario is based in the San Francisco Bay Area

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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Kshira has been with the Analytics/Decision Sciences industry for almost a decade now having worked across Americas, Asia, Europe and Australia. He is the Head of Analytics and Data Science at The Iconic.

We speak about:

-Why he moved from analytics consulting to building data products -What a data driven product should do and how to prioritise your efforts -How to make analytics less intimidating and more accessible -How to take your stakeholders on the data-driven decision making journey in next the best way -How to structure your team for maximum impact in your organisation -Most common issues and roadblocks in creating a data driven culture and how to overcome them

Show notes: https://www.datafuturology.com/podcast/22

Data Scientist job https://github.com/theiconic/datascientist

Kshira is based in Sydney, Australia

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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Antony stumbled into his love of predictive modelling at the tender age of 10. He started his professional career in biostatistics and then made the switch to corporate Australia to work as Head of Analytics in banking before joining Seek; where today he is the Director of Global Matching and Analytics.

We speak about:

how analytical thinking adds value both in research and in corporate
why you should read an intro to epidemiology textbook
how analytics can take you to any field
why this is the most exciting time to be in analytics
his transition from research to corporate
surprises and rewards of moving into corporate
how to use the constraints you have for your benefit and how to love what you do
the rewards of moving to corporate
what proper use of data looks like
how you can help organisations find the value in their data
how to present and explain the need for experiments in business
the importance of educating your organisation on data science
how to focus on value in your organisation
how to take people on a humble, thought-provoking and non-intimidating journey into the use of data science
how to understand the “grey” in business and appreciate people’s journeys
why you should get close to the sales people in your organisation
thoughts on the very high demand of data scientists
data science for social good and why analytics professionals are like Batman
how to stand out in data science interviews and much, much more!

Antony's textbook recommendations:

Statistical Models in Epidemiology

Statistical Methods in Medical Research, 4th Edition

Applied Logistic Regression - Wiley Series in Probability and Statistics

Case Control Studies : Design, Conduct, Analysis

Epidemiology Principles and methods

Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au

Fyrebox - Make Your Own Quiz!

Antony is based in Melbourne, Australia

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

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Epidemiology Principles and methods

David studied applied physics and began his career as a consultant. He’s had his own company where he created a video asset management & workflow software in the 90s!. Then worked in the education/not-for-profit sector and then went into the finance sector as VP of BI & Data Analysis. Today, he is the Senior Vice President and Head of Data, Analytics and Research at BankMobile.

We discuss: - the insights into large companies from his early days in consulting - why technology provides the “guard rails” for the business - why our roles as data scientists is to make sense of the mess - what’s missing in today’s analytics education and how to learn what you need - what to look for when building a diverse team - the importance of creating a narrative in analytics - the mindset to maintain during your analysis - motivations behind problems with data definitions - how data is like a flashlight

Show notes: www.datafuturology.com/podcast/20

David is based in Providence , Rhode Island, USA

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Vlad started his career in visual effects & computer graphics. He worked on Hollywood blockbusters such as Avatar, Dark Knight, Happy Feet. He currently is Head of Data Science at Wooga which makes June’s Journey (Facebook Game of the Year 2017), Pearl’s Perils, Diamond Dash and many more.

We speak about:

why it’s important to follow your curiosity and what that looks like how to keep learning and stay current in data science
uses of pytorch, the second deep learning python library after tensorflow which is backed by facebook
relevant metrics & analytics in the gaming industry
statistical modelling, machine learning & deep learning in gaming
considerations for deploying ML models to production
the importance of speed in delivery of work & reproducibility of data science
how to keep innovating for your customers
what is the semi-embedded model and much, much more!

Show notes: https://www.datafuturology.com/podcast/19

Vlad is based in Berlin, Germany

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Ahmed started his career at Siemens research, worked in startups, at Google, PayPal, SAS and JPMorgan and has his own machine learning company and now he runs data science at Equinor

We speak about:

benefits of simulations in research and data science how he went from “equation-driven” to “data-driven”
the role of simulations in optimisation, decision-making and automation
uses of simulations and deep reinforcement learning models in the energy industry
how data is used 2-5kms underground below the sea to infer the properties of the ground underneath
lessons from startups and what to look for in people to work with
why it’s important for data science teams to own the engagement of value creation with the customer
how to ensure that your data science team is creating value in your organisation
how to prioritise the work done by your data science team and what to aim for; and much much more!

Show notes: www.datafuturology.com/podcast/18

Ahmed is based in London, UK

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Naomi Clarke started as a graduate in the oil business, since then she's worked in multiple industries, and now she is Head of Data in the finance sector.

Naomi has a strong background on business data arch, business data modelling, data governance. I loved the human-centred perspective that she has taken to her work.

We talk about:

the Management Information Systems (MI or MIS) she created in her early days
the importance of business data models for analytics
the difference between a logical and physical data model and which one is more important
how to define the right meaning of the data in your data models
disruptions in the financial sector that happened overnight
the relationship between deregulation, dematerialisation and digitalisation; and how its affecting industries
the tight link between business, data and culture; and how each one affects the others and much, much more!

Show notes: https://www.datafuturology.com/podcast/17

Naomi is based in London, UK

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In this episode we speak with Apollo Gerolymbos who is the Head of Data Analytics at the London Fire Brigade. We speak about:

the applications of data science in firefighting
how the London Fire Brigade (LFB) uses data to preempt and minimise fires
the end to end data science process at the LFB
how their data affects laws, policies and citizens’ lives
how Natural Language Processing (NLP) and text analysis is used on the reports of the most serious fires to identify new patterns of high risk factors
the importance of identifying bottlenecks and weak points in the availability of your service
why it’s important for data scientists to educate non-data people in their organisations and much, much more!

Show notes: https://www.datafuturology.com/podcast/16

Apollo is based in London, UK

Also, catch me at the Chief Data & Analytics Officer Conference in Melbourne on September 3-5, 2018 https://chiefdataanalyticsofficermelbourne.com

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In this episode we speak with Tony Laing who is the General Manager of Analytics & Data Services at Auto & General. We talk about:

what is the ‘nuts and bolts’ of analytics
what is the data supply chain required in organisations for the delivery of analytics/ML solutions
how to deliver quick wins and strategic projects concurrently
the journey to add significant value in an organisation through analytics
what questions to ask executives to kickstart their data science journey
why is there so much turnover in data science
why data preparation and model building is only 20% of the job
what type of model is the best to drive commercial outcomes
whether ML applied in specific domains is AI or not
the art of data preparation, increases in computing power and automation of data science
what is “the knife fight” of data science and what type of companies can benefit the most

Show notes: https://www.datafuturology.com/podcast/15

Tony is based in Brisbane, Australia

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In this episode we speak with Dr Gabriel Maeztu who is the Co-Founder and & Chief Data Scientist at IOMED Medical Solutions. We talk about:

his background, how he went from medicine to data science and how he combines medicine, data science & entrepreneurship
how to start coding when everyone around you tell you you’re crazy
image processing in medicine, using scikit learn to classify patients
how to use data science to validate what you’re taught in medical school
economical Incentives of the medical system that is probably slowing down progress in the data space
GAFAs: Google Apple Facebook Amazon in medical data
value based care built on data science
NLP/text processing in medicine
current & future data challenges in medicine and much, much more!

IOMED is hiring data scientists! https://angel.co/iomed/jobs/379740-data-scientist

Show notes: https://www.datafuturology.com/podcast/14

Gabriel is based in Barcelona, Spain

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In this episode we speak with Ernesto Bernardo, he is the Chief Product & Marketing Officer at iContainers. We talk about:

whether leaders of tomorrow should be technical or not
how to create autonomous teams that focus on value (ROI) & pay for themselves
how creating a data science team that deliver value in the business forces you to go from a centralised to a decentralised team
why creating a data-driven culture in your organisation requires good marketing and great people skills
how the reporting lines of a data science team and significantly affect the teams’ impact in the business
the importance of networking within your company to drive adoption and change the culture
how to stand out in data science interviews & what managers look for
actionable metrics: focus on what you can control/influence to change the metrics you care about - i.e.: what’s the input/driver

Show notes: www.datafuturology.com/podcast/13

Ernesto is based in Barcelona, Spain

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In this episode we speak with Alessandro Pregnolato, he is the Director of Analytics at Typeform.com. We talk about:

his journey to get where he is,
what is the optimal size of a data science team,
how to use data science in SaaS businesses/startups
the 4 pillars of a great data strategy
how to be an expert generalist in the data space and much more!

Alessandro is a Business Analytics Leader with a love for Data Science. He has over fifteen years experience within the domain of BI, Analytics, Big Data and Machine Learning in international environments. He has strong management and communication skills with a demonstrated ability to work well under pressure with people from a variety of backgrounds.

Show notes: www.datafuturology.com/podcast/12

Alessandro is based in: Barcelona, Spain

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In this episode we speak with Takaharu Tsuda, Practice Director & Head of Data Science at Think Big Analytics. We talk about:

his journey to get where he is,
how applications of data science in many Japanese industries,
the translation of data science into Japanese and why it's hurt the industry!
the 3 layers of skills required in data science and much more!

Show notes: www.datafuturology.com/podcast/11

Tak is based in: Tokyo, Japan

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In this episode we speak with Jonathan Hart, Head of Data Science & Analytics at MullenLowe Profero. We talk about:

his journey working in the US, UK, India and Japan,
how to create great Data Science teams and a great culture,
how to use data science in strategic decision-making
how to work with teams all over the globe and much more!

Show notes: www.datafuturology.com/podcast/10

Jonathan is based in: Tokyo, Japan

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In this episode we speak to Matt McDevitt, Director of Data Engineering at Think Big Analytics. We talk about:

his journey working in the US, UK, Europe and Japan as the company grew,
how big data, open source, data engineering and data science work together,
General Data Protection Regulation (GDPR), Personally Identifiable Information (PII), data lineage
business value, data products and much more!

Matt is one of Think Big’s earliest team members playing many roles to help incubate and build Think Big over its 8-year history into the leading Big Data Analytics Global brand. He helped build from scratch and establish Think Big practices in the United States in Mountain View, Salt Lake City, New York, London and Toyko.

Matt assisted in the development of Think Big’s innovative Velocity Delivery methodology, which integrates Data Engineering and Data Science in 6-week release cycles.

Show notes: www.datafuturology.com/podcast/9

Matt is based in: Tokyo, Japan

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This is a different type episode! This is a recording of a presentation I did to about 300 data scientists in Melbourne, Australia. The theme of the night was Agile Data Science, a passion of mine. In this episode I cover:

the productivity gains an individual and a team can gain using agile methods
how agile is imperfect but very helpful
how I've tweaked agile to fit data science and deliver value with my teams
bust some of the main myths around agile, and much, much more!

The show notes and presentation slides are in https://www.datafuturology.com/podcast/8

I hope you enjoy the episode!

I am based in Melbourne, Australia and currently travelling for a few months!

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In this episode we speak to Sandra Hogan, Group Head of Customer Analytics at Origin Energy. We talk about:

how to successfully apply data science,
how to gauge your stakeholders ability to consume analytics,
what is analytics for good, the importance of mentorship in data science and much more!

Sandra has extensive experience in the Marketing Sciences field, predominantly in re-engineering business processes to maximise customer relationship and customer experience outcomes. She's passionate about translating complex customer data into easy to use tools and processes. Her expertise spans embedding analytical capabilities and data driven decisions into business processes to achieve significant improvements across sales and marketing functions.

Show notes: www.datafuturology.com/podcast/7

Sandra is based in: Melbourne, Australia

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In this episode we speak to Mark Blakey, Ex-Managing Director at his own technology company Mainstream Consulting. We talk about:

how to combine entrepreneurship and data science,
how to create small teams that punch above their weight,
how to scale data-driven products using machine learning and much more!

He founded and led Mainstream Consulting, a technology company, for 20 years. During that time he built a blue-chip client base of top tier high street banking customers including CBA, NAB, ANZ, Barclays Bank, Abbey National plc, Legal and General Bank, Woolwich plc, Mortgage Trust plc, Clydesdale Bank, Yorkshire Bank and many others.

Mark is currently the founder and organiser of popular Melbourne meetup group on applications of machine learning to stock market prediction: https://www.meetup.com/Machine-Learning-Applied-to-Stock-Market-Predictions/

Show notes: www.datafuturology.com/podcast/6

Mark is based in: Melbourne, Australia

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In this episode we speak to Dr Anthony Rea, Chief Data Officer at The Bureau of Meteorology of Australia. We talk about what happens to over 30 petabytes of weather data in one of Australia's largest super computer, how to combine data governance and policies with culture and technology, details of the World Meteorological Organisation or WMO - an international data exchange program, how to create machine learning (ML) and data steward communities in your organisation and much more!

Anthony has a background in remote sensing and physics. During his career he has worked on every aspect of data science and now is an executive leader at the Bureau of Meteorology of Australia.

Show notes: https://www.datafuturology.com/podcast/5

Anthony is based in: Melbourne, Australia

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In this episode we speak to Dr Sam Kharazmi, Head of Data Science at RedBubble. We talk about how to focus on product & users with your data science efforts, how data science can add value to company growth at different stages of the company's journey, how to combine people leadership & tech leadership to better drive business outcomes and much more!

Sam is a Data Science, Analytics and Engineering leader with extensive experience in building teams and data product on small and large scale organisation and data science strategy.

Show notes: https://www.datafuturology.com/podcast/4

Sam is based in: Melbourne, Australia

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In this episode we speak to Dr Jacek Kowalski, Chief Data Scientist at Australian Unity. We talk about pragmatic and realistic data science, data science for startups and corporates, leadership in analytics, what it takes to get analytics projects through in large corporates and much more!

Jacek is a highly experienced ICT manager and technical expert. His areas of expertise include Data Science, Network Analytics, Security, Virtualisation, Mobility, and Identity Management.

His technical leadership and significant input into the business strategy contributed to the success of Azure Wireless and its acquisition by Docomo NTT.

He's worked at a number of research institutions, major corporations and technology startups. He's published scientific papers in the area of Statistics, Network Analytics and holds technology patents.

Show notes: https://www.datafuturology.com/podcast/3

Jacek is based in: Melbourne, Australia

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In this episode we speak to Ben Pattison, Head of Customer Data Science at Medibank. We talk about getting buy-in from stakeholders, how to develop data scientists, the relationship with technology and business, how to define strategic priorities for your data science team and much more!

Ben has over 20 years experience, in the UK and Australia, as an analytical and strategic leader, passionate about driving business and customer value from data and analytics.

He has experience of leading large teams to design and integrate Credit Risk, Data Science, Decision Services and Analytical Marketing solutions into Personal, Consumer Finance, Insurance, Small Business and Wealth operations.

Show notes: https://www.datafuturology.com/podcast/2

Ben is based in: Melbourne, Australia

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In this episode we speak to Dr Eugene Dubossarsky, Chief Data Scientist at AlphaZetta and Principal Trainer at Presciient.com We talk about data literacy, questions to ask your potential employer, his definition of actionable insights and much more!

For upcoming Data Science, Machine Learning and R courses go to: http://presciient.com/current-courses/

Eugene is a Strategic Advisor in all aspects Data Analytics - Capability building, winning support, management and operations.

Community Builder. Creator of a number of data science and analytics communities, interest groups and professional associations

He's the creator of ggraptR, an interactive data visualisation package in R

Show notes: https://www.datafuturology.com/podcast/1

Eugene is based in: Sydney, Australia

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