Leaders of Analytics: Recent Episodes

Jonas Christensen

Leaders of Analytics is a podcast about data-driven decision-making, modern business leadership and the use of data and artificial intelligence in business and society.

Each episode investigates the strategies, tools, techniques and leadership required to succeed in a world increasingly driven by data and analytics.

The show’s guests share their stories and experiences in a way that helps listeners understand the big concepts and small details that make all the difference in today’s world of business.

Every day, like invisible breadcrumbs, we leave trails of personal data scattered across the digital landscape.

Each click, every search, every purchase - they all tell a story about us.

But do we know where these breadcrumbs lead? Who's picking them up? And most importantly, what are they doing with them?

In an era where data is documenting our lives across a host of platforms, understanding these trails and their implications is no longer a luxury but rather, a necessity.

It's about our privacy, our rights, and our well-being in an increasingly interconnected world.

In this episode of Leaders of Analytics John Thompson and I dive into his newly released book that should be on everyone's reading list - "Data for All".

During our discussion, we'll delve into the eye-opening insights Thompson shares in his book, such as understanding the scope and consequences of companies manipulating and exploiting your data.

We also explore the step-by-step guide he provides on how to navigate this changing landscape.

We’re definitely in AI hype mode at the moment largely driven by the evolution in generative AI.

However, it seems like this progress is not necessarily driving lots of data-related innovation inside organisations that are not AI-first tech companies.

A recent survey published by Randy Bean’s company, NewVantage Partners, confirms this. Here are the main findings compared to when the survey was last run 4 years ago:

59.5% of executives say their companies use data for business innovation – the same as four years ago.
A drop from 47.6% to 40.8% of executives say their companies compete using data and analytics.
Fewer executives (39.5% down from 46.9%) say their companies manage data as a business asset.
Only 23.9% of executives now say their companies are data-driven, compared to 31% before.
Just 20.6% of executives report having a data culture in their companies, down 27% from 28.3% in 2019.

These numbers spell regression, not progress.

Why is it so hard to become a truly data-driven organisation?

In this episode, Randy and I explore the challenges facing Chief Data & Analytics Officers and their teams, including:

How organizations can create an environment that encourages innovation in data-driven initiatives
Examples of organisations doing data well, and why
How to set clear expectations around the responsibilities of CDAOs
The most important qualities for someone in the CDAO role, and much more.

Randy on LinkedIn: https://www.linkedin.com/in/randybeannvp/

Randy's website and book, 'Fail Fast, Learn Faster': https://www.randybeandata.com/book

In a world where data is the new oil, being able to understand, analyse and interpret it is a vital skill. As the saying goes, "knowledge is power," and in this case, data literacy is the key to unlocking that power.

I argue that data literacy is as important to individual and organisational success as computer literacy, but unfortunately that is not a consensus view. For many organisations and their leaders, low data literacy is hampering their ability to make effective, data-driven decisions.

What is the key to creating a data literate organisation and unlocking the true potential of your data?

Who better to guide us through the many aspects of this question than data literacy expert Kevin Hanegan.

Kevin is the Chief Learning Officer at Qlik and a renowned author of the books “Data Literacy in Practice” and “Turning Data into Wisdom”.

In this episode of Leaders of Analytics, Kevin will be sharing invaluable insights and expertise from his books and his work at Qlik.

Listen in as we explore:

How data literacy can transform businesses, boost individual careers, and help us make better-informed decisions
Practical tips and strategies for developing data literacy skills
Common misconceptions or challenges that hold people back from becoming data literate, and how to overcome these
How to foster a data-driven culture within organisations, and much more.

Kevin's website: https://www.kevinhanegan.com/

Connect with Kevin on LinkedIn.

Learn more about the Data Literacy Project.

This episode of Leaders of Analytics features Dhiraj Rajaram, the Founder of global decision sciences company Mu Sigma. Mu Sigma serves more than 140 of the Fortune 500 and the company’s mission is to simplify complex problems through the science of decisions.

Dhiraj shares his views on problem-solving in business, and how Mu Sigma's three core beliefs have been instrumental in the company's success.

At Mu Sigma, they believe in "Learning over Knowing", "Extreme Experimentation", and "The New IP". Their data-driven decision-making approach has helped solve some of the toughest business challenges and has set them apart from the competition.

As an entrepreneur or business leader, you'll gain valuable insights into using data to solve complex issues, as well as an insider's perspective on Dhiraj's entrepreneurial journey.

In this episode we discuss:

Dhiraj’s entrepreneurial journey from a one-man band to leading thousands of employees
The critical moments that led Dhiraj to become a founder and the key elements of entrepreneurial success
Mu Sigma’s unique recruitment and training strategy
What you can learn from Mu Sigma’s three core beliefs
How to make better decisions for your organisation, and much more.

Mu Sigma's website

Connect with Dhiraj on Linkedin.

In this episode of Leaders of Analytics, I am joined by Ada Guan who is one of the most innovative minds in the field of credit decisioning.

Ada is CEO and co-founder of Rich Data Co, a company that helps lenders make informed and accurate credit decisions by leveraging AI and machine learning.

Listen in as Ada sheds light on the role that AI and machine learning can play in transforming the lending industry and what the future may hold for credit decisioning.

In this episode, we'll discuss:

Ada’s entrepreneurial journey
The typical pain points lenders face and how RDC’s unique AI solution solves these problems
What makes RDC’s solution unique and why banks should buy rather than build themselves
How to find product-market fit or an AI product
The additional benefits an AI solution brings over traditional credit scorecards or rules-based decisioning engines, and much more.

Learn more about Rich Data Co here: https://www.richdataco.com/

Connect with Ada Guan on LinkedIn.

Data is revolutionising our world, yet many companies fail to harness its value. What needs to be done for CEOs to see the value of having analytics as part of the executive inner circle?

Unfortunately, many analytics teams struggle to move past the common challenges of fostering analytics literacy, getting executive buy-in for more investment in data and analytics and showcasing the value delivered into the business.

How can analytics leaders make their discipline an indispensable superpower in their organisation? In this episode of Leaders of Analytics, long-time analytics C-suite executive Murli Buluswar gives us the formula for success.

Murli is Head of Analytics, US Consumer Bank at Citi, and leads a team of almost 600 analytics professionals. He reports directly to the CEO and his team is responsible for supplying the rest of the organisation with insights and data-driven solutions that lead to better customer experience and engagement.

In this episode of Leaders of Analytics, Murli explains:

How to position an analytics function as a key strategic enabler
How Citi’s analytics department picks and validates the most valuable use cases to work on
How to foster the skills and organisational discipline to push analytics into the rest of the organisation
How to measure and communicate an analytics team’s impact on the company and its customers
What’s required of analytics leaders to elevate their function to the C-suite, and much more.

Murli Buluswar on LinkedIn

Previous episode: Why Sport is Leading the Analytics Revolution with Ari Kaplan

“Being an entrepreneur is basically like going from one crisis to the next”. Those are the words of Michael Kingston, co-founder and CEO of Seeda.

At a point in his career when Michael was thriving, he took the daunting plunge from successful executive to entrepreneur and start-up founder. Most people would be too scared to take such an enormous risk; however, this step has been Michael's key toward work-life satisfaction.

Three years later, Michael and his co-founders have built Seeda, an AI-assisted marketing analytics product, purpose-built for Shopify-based eCommerce platforms. Seeda helps marketers make sense of the enormous amount of data coming at them from numerous sources and use it to optimise their marketing activities.

Whether it’s SEO, email marketing or digital advertising, marketers are often stuck with a heavy burden of technical9 implementation and optimisation. Seeda’s product is the “AI analyst” that helps the world’s 5 million Shopify stores figure it all out, without needing to be a technology or analytics expert.

If you’re curious about start-up life or are thinking about starting your own business, then this episode is for you!

In this episode we discuss:

How Michael gradually but surely made the shift from employee to entrepreneur
How Michael figured out what he wanted to work on as an entrepreneur
How Seeda’s “AI analyst” is a potential game-changer for Shopify-based businesses wanting more out of their marketing efforts
The scaled data architecture that allows small businesses to take advantage of data practices normally reserved for large corporates
Michael’s advice for anyone wanting to start their own business, and much more.

Michael on LinkedIn: https://www.linkedin.com/in/michael-kingston-35707217/

Check out Seeda: https://www.seeda.io/

Digital transformation is rapidly changing the way we live and work, and governments should be leading the way forward, according to Victor Dominello MP.

As the Minister for Digital and Minister for Customer Service in the State Government of New South Wales in Australia, Victor believes government should be playing a central role in fostering a digitally-enabled economy across government, private enterprise and individual consumers.

Victor is a true servant leader and an inspirational figure in Australian politics, having served almost 15 years in the State Parliament of New South Wales, and 12 of those as a Minister. He has spent this time turning his vision for data and digital enablement into reality across a large number of ministries and government agencies.

In this episode we discuss:

How Victor went from reluctant politician to long-serving minister and the sign from above that made him enter politics
What the Digital Government is and how it will help change our lives for the better
Government’s role in digitising small businesses
Imminent initiatives to protect consumers against identity theft and cyber attacks
What true servant leadership and customer service looks like
How to provide leadership and collaboration across a complex web of government entities
The biggest leadership lessons Victor has learned as a top politician and executive leader, and much more.

Victor Dominello on LinkedIn: https://www.linkedin.com/in/victordominello/

As the digital landscape evolves, privacy concerns and regulations are becoming increasingly important for advertisers. With the decline of third-party cookies and the rise of individual data usage consent, measuring advertising attention is more crucial than ever.

One of the biggest challenges for advertisers in a cookie-less world is being able to accurately measure the effectiveness of their campaigns. Without cookies, it's harder to track user behaviour and understand how their ads are performing.

However, measuring advertising attention through alternative methods such as viewability, brand lift studies, and surveys can be helpful, but they provide vague and delayed signals about advertising effectiveness.

How can advertisers measure the attention and effectiveness of their advertising in real-time?

To answer this question, I recently spoke to John Hawkins, Chief Scientist at Playground XYZ. Playground XYZ provides a machine learning-based platform for measuring and maximising attention on digital ads.

The company’s Attention Intelligence Platform is a unique technology that uses over 40 different signals to track user attention as it happens.

In this episode of Leaders of Analytics, we discuss:

How Playground’s attention measurement platform works in practice
The importance of attention time in a world without cookies, where privacy and consent are increasingly of mandated importance
Dealing with the complexities of multi-layered machine learning pipelines and convincing stakeholders of their value
How data science professionals can foster the right non-data science skills that will make them true unicorns, and much more.

John on LinkedIn: https://www.linkedin.com/in/hawkinsjohnc/

John's book, Getting Data Science Done.

Inflation is rising, interest rates are up across the globe and cash is king again. How will this impact the flow of venture investments in start-ups and emerging technologies?

While traditional investments may suffer during a recession, the venture capital industry has historically been able to weather the storm and even thrive. One reason for this is that venture capital firms typically invest in early-stage companies that are not yet generating significant revenue.

In fact, some of the most successful companies in recent history, such as Uber, Airbnb and Snapchat, were founded during economic downturns. The downturns created opportunities for entrepreneurs to innovate and create new solutions to problems caused by the economic conditions.

Mendoza Ventures is one such investor, but with a unique approach. Mendoza’s investment strategy is focused on the verticals of AI, fintech and cybersecurity and 80% of their investments go to founders from diverse and minority groups.

I recently caught up with Scott Heyes, CFO at Mendoza Ventures to understand how a venture capital firm works in practice and how he and his colleagues think about investing in the current economic climate and beyond.

In this episode of Leaders of Analytics, we discuss:

How Scott became the CFO at Mendoza Ventures and what a week in venture investing looks like
How the firm decides which companies to invest in
Why Mendoza Ventures specifically back founders from diverse and minority backgrounds.
Which segments within AI, fintech and cybersecurity will win or lose during a period of uncertainty, inflation, reduced access to funding and higher borrowing costs.
The trends in AI, cybersecurity and fintech worth watching in the next 2-5 years, and much more.

Scott on LinkedIn: https://www.linkedin.com/in/scottheyes/

Mendoza Ventures: https://mendoza-ventures.com

Learn more about Annual Recurring Revenue in this episode.

30 years of “corporate social responsibility” has left our planet in dire straits.

Biodiversity loss, climate change, water pollution, micro-plastic pollution, air pollution, species collapse, ecosystem collapse…the list goes on.

What can we all do individually and collectively as business leaders and responsible humans to turn the situation around?

According to Simon Schillebeeckx from Handprint.tech it is possible to create incremental financial value while regenerating the ecosystems we rely on.

Simon and his colleagues at Handprint have written a manifesto for saving the planet, called Regeneration First, that tells us exactly how this can be done.

In this episode of Leaders of Analytics, we discuss:

The current state of the many environmental issues facing us.
The “Regeneration First” manifesto and the 7 action shifts needed in our approach to sustainability.
Whose role it is to deal with climate change
Promising climate technologies that will help us solve the negative impacts we’re having on the planet
How we create more short-term environmental incentives to deliver long-term impact
What we can do individually to contribute to environmental regeneration, and much more.

Links:

Simon on Linkedin: https://www.linkedin.com/in/simonschillebeeckx/

Some promising carbon removal solutions discussed on the A16Z podcast.

The Road to 100 Percent Renewables in Australia via Energy Insiders.

How does a traditional bricks-and-mortar retailer transform itself into an omni-channel business with strong digital and data science capabilities?

In this episode of Leaders of Analytics we learn from Bunnings General Manager, Data and Analytics, Genevieve Elliott, how the company is transforming its operations using data and analytics.

As Australia and New Zealand’s largest retailer of home improvement products, Bunnings is a highly complex organisation with a large physical footprint, a wide product range and an elaborate supply chain.

Bunnings is almost 130 years old and has undergone tremendous growth over the last three decades. The company’s well-known strategy of “lowest price, widest range and best customer experience” is increasingly being driven by the company’s growing data and analytics capability.

In this episode we discuss:

Genevieve’s career journey and how she ended up in data and analytics
How Bunnings uses data to create operational efficiencies, improve customer experience and optimise pricing
How the team prioritises projects and engages with the organisation
How the Data & Analytics team is driving a data-driven culture through the company
Genevieve’s advice to other analytics leaders wanting to drive strategically important results for their organisation, and much more.

Genevieve Elliott on LinkedIn: https://www.linkedin.com/in/genevieve-elliott/

Is your company good at customer success and retention? Chances are that you could be better.

For most businesses with a recurring revenue model, customer churn is a very costly affair. Whenever a customer leaves, you lose out on recurring revenue, forgo the opportunity of expansion (cross sell) revenue and have to pay for another round of acquisition costs to cover the loss.

In my personal experience, customer retention is both art and science. Machine learning and other data science techniques can be used to identify customers who are likely to churn, but it is equally important to craft meaningful and delightful interactions throughout the customer lifecycle.

So, what’s required to become a lean, mean retention machine?

In this episode of Leaders of Analytics, I speak to Sami Kaipa to learn the best practices of data-driven customer retention. Sami is an experienced technology executive, serial entrepreneur and start-up advisor. He is co-founder of Tingono, an AI-driven customer retention platform.

Listen to this episode as we discuss:

Sami's journey as an entrepreneur and corporate technology executive
The core elements of customer success and retention that every business should master
A deep dive into the concepts of customer retention, expansion and NRR
The economics of customer retention and expansion
How data science and machine learning can help with retention, and much more.

Connect with Sami on LinkedIn: https://www.linkedin.com/in/samkaipa/

Tingono's blog: https://www.tingono.com/blog

Do you really need a data-driven culture? Maybe not.

According to Bill Schmarzo, the CEO’s mandate is to become value-driven, not data-driven. For analytics teams that means one thing: no one cares about your data, they want results!

In this episode of Leaders of Analytics, Bill and I explore the economics of data & analytics and how to drive powerful decisions with data. Decisions that turn into business value.

Bill is the author of four text books and one comic book on generating value with analytics. He is a long-serving business executive, adjunct professor, university educator and global influencer in the sphere of big data, digital transformation and data & analytics leadership.

In this episode of Leaders of Analytics, we discuss:

Why Bill has split his career between corporate leadership and education
What value engineering is and how it pertains to data and analytics
How to determine the economic value of data and analytics
Why data management the single most important business discipline in the 21st century, and much more.

Bill's website: https://deanofbigdata.com/

Bill on LinkedIn: https://www.linkedin.com/in/schmarzo/

Bill on Twitter: https://twitter.com/schmarzo

Great analytics teams understand that they are responsible for two things concurrently: production and consumption.

Most analytics teams master the production part well. After all, that’s why they exist, to produce analytics.

However, analytics only matter if someone consumes them and makes valuable decisions as a result. “Decision + value” is what we’re after.

To be able to make valuable decisions from analytics, consumers must be data and analytics literate, and that often comes down to education and culture creation.

So, how do you build analytics literacy in your organisation?

In this episode of Leaders of Analytics, Ben Jarvis, Head of Scaled Customer Services and Operations AUNZ at Google, answers this question and many more related to building a strong analytics culture.

Listen to learn:

How Ben went from practicing law to becoming a senior analytics leader and operational GM
How to coach and mentor technical and non-technical stakeholders on data and analytics literacy
How do traditional businesses that aren’t born out of the internet era can transform into data-driven and analytics-literate organisations, and much more.

Connect with Ben on LinkedIn: https://www.linkedin.com/in/ben-stuart-jarvis/

If you’re anything like me, you have a love/hate relationship with marketing. Marketing can be delightful, obnoxious or somewhere in-between, depending on content and context.

Most of us remember an ad from our youth that has given us a life-long emotional connection to a brand or product. Most of us also remember that obnoxious sales call or email campaign that made us swear never to buy from the offending company again.

In this episode of Leaders of Analytics, you will learn from Ikechi Okoronkwo why data-driven marketers have a leg-up when it comes to designing and executing impactful campaigns that hit the right audiences and create delight.

Ikechi is Executive Director, Managing Partner and Head of Business Intelligence & Analytics at Mindshare. Mindshare is a global media and marketing agency, and part of global marketing powerhouse GroupM.

Listen to this episode to learn:

What Ikechi sees as the biggest opportunities in data-driven marketing
What kinds of analytics to invest in to optimise the impact of your marketing efforts
What kinds of data is needed to take advantage of these opportunities, and how to collect it
How Ikechi and colleagues use data and analytics to distinguish between rational and emotional reactions to advertising
How to drive a culture of experimentation and measurement among colleagues and stakeholders who are more creatively than analytically minded, and much more.

Ikechi on LinkedIn: https://www.linkedin.com/in/ikechi-okoronkwo-0318579/

Business leaders are changing.

Today, it’s not enough to be a strategic thinker and good people leader to be successful in the corporate world. Why?

Modern business leaders are customer-centric and understand how to create a personalised customer experience using customer data.

Modern business leaders are data-driven and understand how to make decisions based on probabilistic outcomes, not just gut feel.

Modern business leaders understand what it takes to develop and deploy artificial intelligence in their organisation.

So, how do we educate our future business leaders to be analytics literate, technically capable and able design and use AI effectively and responsibly?

I recently spoke to Professor Hind Benbya to answer this question and many more relating to educating our future business leaders.

Hind is the Head of the Department of Information Systems & Business Analytics at Deakin University, where she leads the strategic direction of the department as well as academic aspects of teaching, research and industry engagement.

In this episode of Leaders of Analytics, you will learn:

The critical must-learn skills for students wanting to shape the future of business with data and analytics
The role of data, analytics and AI in business 10 years from now and how today’s business leaders must prepare
How we bring today’s business leaders and executives up to speed with data and analytics
How analytics leaders can drive their organisations to become truly data-driven, and much more.

Hind on LinkedIn: https://www.linkedin.com/in/hindbenbya/

Hind's research and publications: https://scholar.google.com/citations?user=KNAW0xsAAAAJ&hl=en

Deakin's Department of Information Systems & Business Analytics: https://www.deakin.edu.au/business/department-of-information-systems-and-business-analytics

Most of us take for granted that food is always available to us when we need it. Our local supermarkets have shelves stacked with produce from all corners of the world. Rarely do we stop to think that the items in our shopping carts have been on a long journey involving months of work by many people.

How does all this food get produced in the first place, reliably, consistently and to a high standard? How do we combine and utilise scarce resources to feed billions of people around the world every day?

I recently caught up with Serg Masis to answer these questions and understand how data science is used to optimise food production around the world.

Serg is a Climate & Agronomic Data Scientist at global agriculture company Syngenta and author of the book ‘Interpretable Machine Learning with Python’.

In this episode of Leaders of Analytics, we discuss:

The biggest challenges facing our global food system and how data science can help solve these
How data science is used to help the environment
Why Serg wrote the book ‘Interpretable Machine Learning with Python’ and why we should read it
How to make models more interpretable, and much more.

Connect with Serg:

Serg's website: https://www.serg.ai/#about-me

Serg on LinkedIn: https://www.linkedin.com/in/smasis/

Serg's books from Packt: https://www.packtpub.com/authors/serg-masis

Professional sports have undergone a true data revolution over the last two decades.

Today, all major sports teams, regardless of sports code, use analytics and data science to drive team performance, optimise game outcomes and scout young talent.

Why has analytics become so popular in professional sports and how does it help drive a competitive edge?

To answer these questions and many more relating to the sports analytics, I recently spoke to Ari Kaplan.

Ari has spent more than three decades using analytics to measure and understand human ability, scout future superstars and win professional sports titles.

He is known as “The Real Moneyball Guy” because of his work in baseball and his involvement in making the Hollywood classic Moneyball.

Today, Ari is Global AI Evangelist at DataRobot.

Listen to this episode of Leaders of Analytics to learn:

How Ari became “the Real Moneyball Guy”
The analytics the Chicago Cubs used to break a 108-year drought by winning the World Series in 2016
The evolution of analytics and data science in sports
What the business world can learn from sports in terms of using analytics to gain a competitive edge
Where sports analytics is going in the future, and much more.

It’s no secret that data and analytics can be used to create a competitive advantage for almost any modern business.

In fact, the customer data you capture in the course of doing business is one of the strongest differentiators between you and the competition.

So, how do we build an organisation that is capable of both producing and consuming truly differentiating data products?

It’s not enough to just have a great analytics team that is capable of producing high quality work. We also need an organisation that is able to consume this output, however advanced it might be.

Back by popular demand, analytics executive and author of ‘Building Analytics Teams’ John Thompson is returning to Leaders of Analytics to talk about the future of analytics leadership.

In this episode, we discuss:

Where analytics teams should sit in the organisational structure
The typical mistakes businesses make when designing analytics teams and embedding them in the organisation
How we plant the seed of advanced analytics and build a data-driven culture
How we select and prioritise the right data and analytics projects to work on
The main purpose and remit of a Chief Data & Analytics Officer
What the perfect data-driven organisation looks like, and much more.

John on LinkedIn: https://www.linkedin.com/in/johnkthompson/

John's book 'Building Analytics Teams': https://www.packtpub.com/product/building-analytics-teams/9781800203167

Defensive vs. offensive data & analytics: https://hbr.org/2017/05/whats-your-data-strategy

Every company, regardless of size, is dealing with a barrage of data. In any typical organisation, there is more information on hand than we know how to use or manage.

While every team in the organisation is screaming for analytics professionals to turn data into insight, a strong data and analytics tech stack is foundational to being able to make sense of it all.

The need for a robust and efficient data and analytics tech stack has created a sprawling industry for new technology solutions that sell the promise of seamless integration and faster insights.

Today, there are a plethora of data and analytics platforms available, most with very high valuations attached to them.

But do we really need all these tools to make us super-powered data users?

To answer this question and many more related to the data and analytics tech stack, I recently spoke to Benn Stancil.

Benn is the co-founder and Chief Analytics Officer at Mode. Mode is a modern analytics and BI solution that combines SQL, Python, R and visual analysis to answer questions for its users.

In this episode of Leaders of Analytics, you will learn:

What the perfect analytics tech stack looks like and why.
Programmatic automation of the analytics workflow.
What will cutting-edge analytics tech be able to do 5-10 years from now.
Why Been thinks the Chief Analytics Officer role should be redefined, and much more.

Connect with Benn

Benn on LinkedIn: https://www.linkedin.com/in/benn-stancil/

Benn on Twitter: https://twitter.com/bennstancil

Benn's (brilliant) Substack blog: https://benn.substack.com/

In a recent conversation with data warehousing legend Bill Inmon, I learned about a new way to structure your data warehouse and self-service BI environment called the Unified Star Schema.

The Unified Star Schema is potentially a small revolution for data analysts and business users as it allows them to easily join tables in a data warehouse or BI platform through a bridge.

This gives users the ability to spend time and effort on discovering insights rather than dealing with data connectivity challenges and joining pitfalls.

Behind this deceptively simple and ingenious invention is author and data modelling innovator Francesco Puppini.

Francesco and Bill have co-written the book ‘The Unified Star Schema: An Agile and Resilient Approach to Data Warehouse and Analytics Design’ to allow data modellers around the world to take advantage of the Unified Star Schema and its possibilities.

Listen to this episode of Leaders of Analytics, where we explore:

What the Unified Star Schema is and why we need it
How Francesco came up with the concept of the USS
Real-life examples of how to use the USS
The benefits of a USS over a traditional star schema galaxy
How Francesco sees the USS and data warehousing evolving in the next 5-10 years to keep up with new demands in data science and AI, and much more.

Connect with Francesco

Francesco on Linkedin: https://www.linkedin.com/in/francescopuppini/

Francesco's book on the USS: https://www.goodreads.com/author/show/20792240.Francesco_Puppini

Modern analytics teams are central business functions directly and indirectly responsible for increasing revenue, reducing costs, optimising processes and improving customer and employee satisfaction.

But there are many obstacles along the way. Data needs collecting, projects need careful design and execution and stakeholders need convincing. Analytics teams are required to cover a wide range of technical knowledge, business acumen and leadership skills to be impactful.

What is the recipe for creating analytics teams that deliver impactful solutions and drive real business value? What are the technical, interpersonal and leadership skills required to lead the business through change and adoption of analytics?

To answer these questions, and many more relating to the art and science of building excellent analytics functions, I recently spoke to John K. Thompson.

John is an international data and technology executive with over 30 years of experience in business intelligence and advanced analytics and author of the best-seller ‘Building Analytics Teams’.

In this episode of Leaders of Analytics, we discuss:

The hallmarks of an excellent analytics team
What a perfect analytics team looks like
The skills, personality traits and behaviours you need in an analytics team
The common traits of highly effective analytics leaders
How analytics leaders set themselves up to meet the expectations of business stakeholders
How to select and prioritise the right projects to work on
Where organisations typically fail when designing analytics teams
The lowdown on John’s upcoming book, and much more.

John on LinkedIn: https://www.linkedin.com/in/johnkthompson/

John's book 'Building Analytics Teams': https://www.packtpub.com/product/building-analytics-teams/9781800203167

There is so much to learn!

If you’re anything like me, you’re overwhelmed by the number of books, articles, podcasts, online and offline courses, webinars and other training opportunities out there.

Today, we’re not short of learning materials, but often lack the time and capacity to learn new things.

But what if there’s a better way to learn?

Enter the concept of “Ultralearning”, coined by best-selling author Scott Young.

A few years ago, I read Scott’s book Ultralearning and it changed my life. Not only did Scott’s approach to learning increase my learning rate significantly, it also made the process a lot more enjoyable overall!

Scott is an impressive Ultralearner who has used his advanced learning strategies to complete a 4-year computer science degree in 12 months, learn languages such as Spanish, Chinese, Korean and Macedonian and become a decent portrait artist.

And then he’s written a book about it.

In this episode of Leaders of Analytics, you will learn:

How Scott has used his learning principles to master very complex and diverse skills in a very short time
How we learn and retain information
How we can structure our learning to faster absorption and better retention
How Scott designs a learning strategy from scratch
Whether Malcolm Gladwell’s “10,000 hour rule” is true or BS
Strategies for learning hard and soft skills, and much more.

Scott's website (full of excellent learning resources): https://www.scotthyoung.com/

Scott's podcast: https://www.scotthyoung.com/blog/podcast/

Scott on Twitter: https://twitter.com/scotthyoung/

Scott on LinkedIn: https://www.linkedin.com/in/scott-h-young-867ab21/

An estimated 80 to 90 percent of the data in an enterprise is text. Sadly, this rich information is mostly neglected for analytical purposes.

Textual data is typically full of information, but also very complex to interpret computationally and statistically. Why?

Because textual data is both content and context. The same words and sentences can have very different meanings depending on the context.

Textual data is truly a goldmine, but how can we mine it without being digital superpowers like Google, Microsoft or Facebook?

To answer this question and many more relating to interpretation of textual data, I recently spoke to Bill Inmon.

Bill is the Founder, Chairman and CEO of Forest Rim Technology and author of more than 60 books on data warehousing. He is often described as the Father of Data Warehousing due to his pioneering efforts in making data and data technologies available to organisations across all industries and sizes.

In this episode of Leaders of Analytics, we discuss:

How Bill became the Father of Data Warehousing
The history of data warehousing and the most exciting developments in this space today
The typical challenges holding us back from extracting value from textual data
The concept of the “Textual ETL” and it’s benefits over other text data storage and analytics approaches
Why NLP is not the best approach for textual data analytics
The biggest opportunities for textual analytics today and in the future, and much more.

Connect with Bill:

Forest Rim Technnology: https://www.forestrimtech.com/

Bill on LinkedIn: https://www.linkedin.com/in/billinmon/

This is the second episode of a two-part series of Leaders of Analytics featuring global data science thought leader and influencer Felipe Flores.

Felipe is a global thought leader and influencer in the field of data science and artificial intelligence.

He is the founder of Data Futurology – a podcast and events company with more than 10,000 weekly listeners, Head of Data & Technology at Honeysuckle Health and co-organiser of Data Science Melbourne.

In this episode we discuss:

Felipe’s work at Honeysuckle Health
What Honeysuckle Health does and why the company was founded by two large insurance organisations
How data-driven personalised health care works in practice and the typical outcomes patients see

How data will be used to drive positive health outcomes in the future, and much more.

Automated decisions, personalised customer and employee experiences and data-driven decision-making are at the core of digital transformation in the 2020s.

In other words, data is eating the world and all modern leaders must know how to use data, analytics and advanced data science to power their organisations.

So, how do organisations set themselves up for success in a data-driven world, technically and culturally?

To answer this question and many more relating to data-driven innovation and intrapreneurship, I recently spoke to Felipe Flores.

Felipe is a global thought leader and influencer in the field of data science and artificial intelligence. He is the founder of Data Futurology – a podcast and events company with more than 10,000 weekly listeners, Head of Data & Technology at Honeysuckle Health and co-organiser of Data Science Melbourne.

In this first episode of a two-part series of Leaders of Analytics featuring Felipe, we discuss:

Felipe’s journey from a young backpacker to a global data science executive
What Data Futurology does and why Felipe started it
How to innovate with data science
The biggest trends in data science in the next 1-3 years
What the perfect data-driven organisation looks like and much more.

Felipe on LinkedIn: https://www.linkedin.com/in/felipe-flores-analytics/

Data Futurology: https://www.datafuturology.com/

Honeysuckle Health: https://www.honeysucklehealth.com.au/

When we talk about data and AI ethics, we typically view this through a privacy lens.

That is, someone’s personal data has either been compromised and ended up in the wrong hands, or personal data is used to manipulate or create adverse outcomes for individuals or minority groups.

These factors are still fundamental to AI ethics, but there is now also a big focus on the broader social impact of AI, including human rights, data privacy and using AI for good.

Enter the concept of data pollution.

The data pollution paradigm describes how the use and intentional or unintentional sharing of personal data can create social harm – not just private harm affecting only the individuals included in the dataset.

To understand the concept of data pollution and its impact on individual privacy and society as a whole, I recently spoke to Gianclaudio Malgieri.

Gianclaudio is Associate Professor of Law and Technology at the Augmented Law Institute of EDHEC Business School (Lille, France), Co-Director of the Brussels Privacy Hub, lecturer in IP and Data Protection and an expert in privacy, data protection, intellectual property, law and technology, EU law and human rights.

In this episode of Leaders of Analytics, we discuss:

The evolution of data and AI ethics over the last 20 years
Why data protection is so important to the future of our society as we know it
What data pollution is and why we should care about it
What we can do to create data sustainability
What business leaders, legislators and legal professionals can do to deal with AI sustainability issues, and much more.

Gianclaudio's website: https://www.gianclaudiomalgieri.eu/

Gianclaudio on LinkedIn: https://www.linkedin.com/in/gianclaudio-malgieri-410718a1/

Brussels Privacy Hub: https://brusselsprivacyhub.eu/

Is the typical hiring and job search process broken?

It is definitely full of bias.

First, we get interested candidates to submit their resumes.

Then someone (typically not the hiring manager) will pick out the resumes that look most interesting to them.

Resumes that survive are typically carefully curated for someone to be able to form a positive opinion in just a few seconds.

Then the hiring manager will pick their favourites out of that smaller pile. At this point, the lion’s share of candidates has been excluded purely based on resumes.

Then comes the first interview. According to a study in the Journal of Occupational and Organisational Psychology, 60% of interviewers make their decision in the first 15 minutes.

What’s more, according to Hubspot, 85% of jobs are filled through networking. We prefer to hire someone we already know, because we think we have an idea of their ability.

We are genetically designed to make quick decisions based on limited data points, which is at odds with very complex decisions such as hiring the right candidate. We try to deal with this through resumes, but these documents are also heavily biased.

How do we limit our own biases and measure all candidates objectively?

How do we identify the rising stars and unique talents who don’t yet have a long resume full of experience?

I recently spoke to Tim Freestone to get an answer to these questions and many more relating to hiring the right data and analytics candidates.

Tim is the founder of Alooba, the world’s first data and analytics assessment platform. Alooba’s tools help organisations around the world objectively assess the skills and capabilities of new candidates and existing team members alike.

In this episode of Leaders of Analytics, we discuss:

The biggest challenges for hiring managers in the data and analytics industry and how we can solve these
The typical mistakes hiring managers and candidates make when they recruit and apply for roles respectively
The biggest opportunities to improve the hiring process for data and analytics professionals
What skillsets make data & analytics candidates stand out in today’s job market
Must-have skills that hiring managers should look for in their candidates, and much more.

Tim Freestone on LinkedIn: https://www.linkedin.com/in/tim-freestone-alooba/

Alooba's website: https://www.alooba.com/

Blockchain technology, cryptocurrencies and decentralised finance are described by some as massively disruptive technologies that will turn our existing financial system on its head.

For the traditional financial services industry, these technologies have the potential to create huge efficiency gains and democratise more complex financial services for individual users.

On the other hand, DeFi also reduces – and potentially removes – the need for trusted intermediaries, which makes the model unsettling to some operators in the current financial system.

DeFi also opens the opportunity for global financial inclusion of enterprises and private individuals in developing markets – a very large group whose needs are typically unmet by traditional finance.

With all this huge potential about to be released, we better learn why these technologies are so revolutionary and what will they do for us now and in the future.

To answer these questions and many more relating to DeFi, I recently spoke to Daniel Liebau. Dan is the Chief Investment Officer, Blockchain Strategy at Modular Asset Management and the Founding Chairman of Lightbulb Capital, a DeFi investment and consulting firm.

In this episode of Leaders of Analytics, Dan and I discuss:

Why is DeFi so revolutionary and the opportunities and risks that lie within this space for individual users, corporations and nation states
The difference between Payment, Utility and Security tokens and how these are likely to be used in our future financial system
The utility of NFTs and their future as an asset category
How blockchains, cryptocurrencies and DeFi will be part of our lives in 5, 10 and 20 years respectively
What Dan is teaching his FinTech, crypto and DeFi students, and much more.

Daniel Liebau on LinkedIn: https://www.linkedin.com/in/liebauda/

Lightbulb Capital: https://www.lightbulbcap.com/

Data is everywhere, but do we know what it means?

A common problem for many enterprises wanting to adopt cutting edge, data-driven solutions is that they have a ton of legacy applications interlinking with more modern tech stacks.

If the organisation is large or complex enough, it typically becomes unrealistic for any one individual to understand how it all hangs together.

All of these applications generate data points with their own definitions, meaning and naming conventions.

How do organisations like these set themselves up for success in a data-driven world, technically and culturally?

How can we create a consistent and holistic view of our data that can be used equally by technologists, analysts and business users?

To answer these questions, I recently spoke to David P. Mariani who is the founder and Chief Technology Officer of AtScale. Dave is an incredibly talented technology executive and entrepreneur with more than $800 million worth of company exits on his resume.

In this episode of Leaders of Analytics, we discuss:

How to create successful technology companies from scratch
What David learned during his time at Yahoo! that made him start AtScale
What a semantic layer is and what it does for your organisation
What David’s utopian technology stack would look like and why
David’s vision for how data-driven organisations will function in the future
How a universal semantic layer fits into this future, and much more.

David's LinkedIn: https://www.linkedin.com/in/davidpmariani/

AtScale's company website (lots of great content on here): https://www.atscale.com/

Ever heard of ‘synthetic data’?

Synthetic data is data that is artificially created (from statistical models), rather than generated by actual events. It contains all the characteristics of production data, minus the sensitive stuff.

By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated, according to Gartner.

The reason organisations may use synthetic data over actual data is because you can get it more quickly, easily and cheaply.

But there are concerns with this approach, because synthetic data is based on models and algorithms designed by humans and their biases.

More data doesn’t necessarily equal better data.

Is synthetic data a brilliant tool for improving data quality, reducing data acquisition costs, managing privacy and reducing overfitting?

Or does synthetic data put us on a slippery slope of hard-to-interrogate models that are technically replacing fact with fiction?

To answer these questions, I recently spoke to Minhaaj Rehman, who is CEO & Chief Data Scientist at Psyda, an AI-enabled academic and industrial research agency.

In this episode of Leaders of Analytics, you will learn:

What synthetic data is and how it is generated
The most common uses for synthetic data
The arguments for and against using synthetic data
When synthetic data is most helpful and when it is most risky
How to implement best practices for mitigating the risks associated with synthetic data, and much more.

Episode timestamps:

00:00 Intro

03:00 What Psyda Does

04:23 Academic Work and Modern Education

06:38 Getting into Data Science

11:30 What is Synthetic Data

13:30 Common Applications for Synthetic Data

18:50 Pros & Cons of using Synthetic Data

21:29 Risks of using Synthetic Data

23:48 When should Synthetic Data be Used

29:23 Synthetic Data is Cleaner than Real Data

34:05 Using Synthetic Data for Risk Mitigation

36:05 Resources on Learning More about Synthetic Data

38:05 Human Biases in Decision Making

Connect with Minhaaj:

Minhaaj on LinkedIn: https://www.linkedin.com/in/minhaaj/

Minhaaj's website and podcast: https://minhaaj.com/

Is AI good or bad?

That would depend on how AI is applied.

AI is a revolutionary capability with the power to do a lot of good and plenty of bad, on purpose or by omission.

In order for AI to become a social good that improves our lives in broad terms, we must necessarily pick the right use cases and design solutions with a strong focus on ethics and privacy.

So, how is AI being used for social good today, and how do we ensure the important topics of ethics and privacy are front and centre for those designing AI solutions?

To answer these questions and many more relating to using data for good, I recently spoke to Dr Alex Antic.

Alex is the Managing Director of the Dr Alex Antic Group and an award-winning data & analytics leader with a truly impressive CV spanning across quantitative finance, insurance, academia, several federal government departments and consulting as well as advisory and board roles.

In this episode of Leaders of Analytics, we cover:

The role data, data science and AI can and should play in society
Examples of how AI is being used for social good
How public entities ensure people’s privacy is maintained, including the use of Privacy Enhancing Technologies
The most important data science and AI skills for us to foster as a society
How Alex is teaching future data leaders to make ethical design choices, and much more.

Dr Alex Antic website: https://dralexantic.com/

Dr Alex Antic LinkedIn profile: https://www.linkedin.com/in/dralexantic/

Data is eating the world and every industry is impacted.

In most modern businesses, customer and employee activities create a plethora of data points and information that can be analysed and interpreted to make better decisions for the business and its customers.

Unfortunately, this sounds a lot easier than it is. Despite the huge mountains of data being created, many organisations struggle to get their business intelligence to serve them in the best way. This is not due to a shortage of reports and dashboard floating around – in many cases there are too many ways to get an answer to the same question.

So, why are so many organisations lacking good BI and what should they do about it?

I recently spoke to Jen Stirrup to get an answer to this question and many more relating to producing and consuming business intelligence effectively.

Jen is the CEO & Founder of Data Relish, a global AI, Data Science and Business Intelligence Consultancy. She is a leading authority in AI and Business Intelligence Leadership and has been named one of the Top 50 Global Data Visionaries and Top 50 Women in Technology worldwide.

In this episode of Leaders of Analytics, you will learn how to avoid data paralysis and discover how to create business intelligence that gives your organisation new superpowers.

Jen's website: https://jenstirrup.com/

Jen's LinkedIn profile: https://www.linkedin.com/in/jenstirrup/

Jen on Twitter: https://twitter.com/jenstirrup

"We’re at a crossroads when it comes to data and its ability to make a difference. Data sprawl has become a real and costly problem inside organizations, and it is hurting innovation.

Throwing good money at bad ideals is no longer acceptable, ROIs must be attained. Let us embrace innovative technology, but let us also keep in mind that data itself is useless unless you do something with it!"

These are the words of Carla Gentry, one of my guests in this episode.

And I agree with her. Data is a strategic asset in most organisations and need to be organised, managed and deployed with the same respect and rigour as a company’s financial capital.

For data leaders it is now incumbent on them to be more than technical specialists. We need to set the vision and agenda, in terms of what data can create for customers and the business. We need to lead our organisations, not just work in them.

In this episode of Leaders of Analytics, we hear from Carla Gentry, Owner and Chief Data Scientist at Analytical-Solution and Whitney Myers, CEO of Zuar on what it takes to succeed with data in 2022 and beyond.

Carla and Whitney are true experts and thought leaders in data-driven business leadership and I trust that you will learn a lot from the two of them, just like I have.

Learn more about Carla here:

Website: https://analytical-solution.com/

Twitter: @data_nerd

LinkedIn: https://www.linkedin.com/in/datanerd13/

Learn more about Whitney here:

Website: https://www.zuar.com/

LinkedIn: https://www.linkedin.com/in/whitney-myers-365b057/

We are living in an artificial revolution where the balance of power and political influence is shifting towards those who control data and technology.

Automation is transforming our economies and making some jobs obsolete.

Companies harvest our most intimate secrets and use them to feed us tailored information and sell us products.

The metaverse is the development of a virtual world with the potential to separate us from the physical world altogether.

AI is making our lives more curated and convenient, but at the same time more complex and exposed.

Privacy and ethics have to be programmed by design to avoid digital versions of oil spills and nuclear disasters.

I recently spoke to Ivana Bartoletti to understand how humanity can tackle this newfound challenge.

Ivana is the Global Chief Privacy Officer at Wipro and an internationally recognised thought leader in the field of data privacy and AI ethics.

She is also the co-founder of the Women Leading in AI Network and the author of the brilliant book on the risks and opportunities of AI, called 'An Artificial Revolution: On Power, Politics and AI'.

In this episode of Leaders of Analytics, we discuss:

Why everyone should give heed to the challenges of privacy, ethics and fairness in a world driven by data
How to balance the trade-off between the benefits of AI and the risks of compromised privacy
How large-scale automation will impact society as a whole
Why data is inherently political
Why woman have a special role to play in making AI fair, and much more.

Learn more about Ivana and her projects here: http://www.ivanabartoletti.co.uk/

Connect with Ivana: https://www.linkedin.com/in/ivana-bartoletti-77b2b29/

When we talk about analytics and AI-driven organisations, we often think of the likes of Google, Amazon, Facebook, Netflix and Tencent, which have all risen to dominance during the internet era.

But what about companies that have been around for much longer, can they achieve the same results with their data?

To answer this question, I recently spoke to Tom Davenport who is one of the world’s foremost thought leaders and authors in the areas of business, analytics, data science and AI.

He is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics.

He has authored more than 20 books and hundreds of articles on topics such as artificial intelligence, analytics, information and knowledge management, process management, and enterprise systems.

He is a regular contributor to Harvard Business Review, Forbes Magazine, The Wall Street Journal and many other publications around the world.

In this episode, Tom gives us a history lesson of data and analytics and provides an in-depth description of what it takes for traditional companies to ascend through what he calls the “Four Eras of Analytics”.

Why is the Data Scientist role called the sexiest job of the 21st century?

I believe it’s partly because the data science profession is constantly evolving to include new data types, new tech and tools, new modelling techniques along with an increasing ability to drive customer and business outcomes with data.

The main challenge for data scientists becomes one of bandwidth. Great data scientists are highly intelligent, technically proficient, curious and creative, but even so, the world of data science is evolving too fast for most individuals to keep up with.

I recently spoke with Ravit Jain to understand how data professionals stay relevant and connected to the fast-paced world of data.

Ravit is a true servant leader who has built a global online community of data lovers.

Through his work as a book publisher, podcast and vlog host, content curator and conference organiser he helps hundreds of thousands of data professionals learn new skills, share knowledge and connect with each other.

In this episode of Leaders of Analytics, we discuss what’s hot in data, including:

How Ravit became passionate about the world of data
How to build your career in data
The most important trends and topics in data today and the future
The traits that make some data science leaders stand out from the rest
Why Ravit’s first advice for aspiring data professionals is to start networking with others in the industry, and much more.

In my opinion, any organisation with respect for its data should have a Chief Data & Analytics Officer (CDAO) as part of their C-suite.

Although the CDAO role is still nascent, business leaders across many industries are starting to appreciate the need for a data and analytics voice at board and executive level.

So, what does a CDAO do?

How should they spend their time to balance strategic influence with operational delivery of data products?

To answer these questions and many more related to the principal analytics role, I recently spoke to Kshira Saagar, who is the Chief Data Officer at Latitude Financial.

As the CDO at one of Australia’s largest consumer financial services firms, Kshira is responsible for the end-to-end journey of data through the organisation, from extraction to value creation through data products.

He leads a large team of Data Scientists, Data analysts, Data Architects, Data Engineers, Machine Learning Engineers, Data Warehouse Developers, BI Developers and Data Governance experts, who are responsible for bringing the company’s data and analytics strategy to life.

In this episode of Leaders of Analytics, we discuss:

What a week in the role of a CDAO looks like
How to secure strategic support and executive sponsorship for analytics projects
What’s required of CDAOs and their teams to foster a data literate organisation
How to structure data and analytics functions for success
The future of the CDAO role, and much more.

Learn more about Kshira at https://www.kshirasaagar.com/

Data science and machine learning are integral parts of most large-scale product manufacturing processes and are used to understand customer needs, detect quality issues, automate repetitive tasks and optimise supply chains.

It’s an invisible glue that helps us produce more things for less, and in a timely fashion.

To learn more about this fascinating topic, I recently spoke to Ranga Ramesh who is Senior Director, Quality Innovation and Transformation at Georgia-Pacific.

Georgia-Pacific is one of the world’s largest manufacturers of consumer paper products and uses AI technologies throughout their manufacturing process.

In this episode of Leaders of Analytics, we explore how computer vision and machine learning can be used to classify tissue paper softness and instantly detect quality issues that could otherwise render large volumes of product useless.

Ranga’s work is featured as a case study in our recently published book, Demystifying AI for the Enterprise.

Data science and machine learning are continuing to evolve as core capabilities across many industries.

But high-quality data science output is only half the story.

As the data science profession matures from “back office support” to leading from the front, there is an increasing need for more integrated systems that plug into business operations.

To get the most out of these capabilities, organisations must move beyond just building robust models, and establish operational processes that can produce, implement and maintain machine learning systems at scale.

Enter MLOps.

To understand the fundamentals and best practices of MLOps, I recently spoke to Shalini Kurapati who is CEO of Clearbox.ai.

Clearbox AI is the data-centric MLOps company that enables trustworthy and human-centred AI.

Their AI Control Room automatically produces synthetic data and insights to solve the issues related to data quality, data access and sharing, and privacy aspects that block AI adoption in companies.

In this episode of Leaders of Analytics, we cover:

What MLOps is and why we need it to succeed with advanced data science solutions
How to get beyond the proof-of-concept-to-production gap and get models into operation
The importance of data-centric AI in building MLOps best practices
The most common AI pitfalls to avoid
How Human Centred Design principles can be used to build AI for good, and much more.

Check out Clearbox here: https://clearbox.ai/

Connect with Shalini here: https://www.linkedin.com/in/shalini-kurapati-phd-she-her-06516324/

“Out of stock”.

Three words with a great deal of significance for retailers and their customers.

It is estimated that retail products are out of stock 8% of the time in physical stores, and more than 14% of the time in e-commerce stores, leading to frustration for retailers and customers alike.

Retailers miss out on important revenue from the forgone sales.

Customers leave unfulfilled and are less likely to return to the same retailer or recommend it to others in their network.

Supply chains feel the ripples of the gaps between demand and supply.

This is a trillion-dollar problem globally.

The solution to this problem is not just about demand forecasting, but also knowing what you have in stock, which is a huge challenge in itself.

To understand how to solve this challenge, I recently spoke to Min Chen who is the co-founder and CEO of Wisy Inc. The company’s technology is focused on reducing retail stockouts and waste with artificial intelligence and data analytics.

Min is a seasoned entrepreneur and an all-round interesting person. Having migrated from China to Panama at age 4, the now lives in Silicon Valley after moving Wisy from Panama to the US in 2020.

In this episode of Leaders of Analytics, you will learn:

How AI can help solve a global, trillion-dollar supply chain problem
How to develop a product-market fit for AI solutions
How to bootstrap a start-up in a difficult environment
Why Wisy decided to move the company from Panama to Silicon Valley

My guest on this episode of Leaders of Analytics is Kate Strachnyi.

Kate is a well-known figure in the global data community. She is a master educator and prolific content creator who has built an online community of almost 200,000 followers. Through the DATAcated brand she runs online training, seminars, conferences, expos and podcasts while connecting data professionals across the world.

She is also the author of four books in the data science genre and a marathon runner.

I recently caught up with Kate to learn more about what it takes to keep up with the fast-paced and ever-evolving world of data and analytics.

In this episode we discuss:

The most important data science skills in the next 5-10 years
The most underrated skill in data science
How to make your day productive and enjoyable
Career advice for someone starting out in data science today
Minting NFTs for the global data community, and much more

You can find more from Kate here:

DATAcated: https://datacated.com/

LinkedIn: https://www.linkedin.com/in/kate-strachnyi-data/

This might just be the most interesting and thought-provoking episode of Leaders of Analytics yet. Why?

Without even recognising it, you make hundreds of ethical decisions every day. Some of these decisions you probably don’t even recognise as being grounded in ethical principles because they are so ingrained in your subconscious.

AI on the other hand, doesn’t make decisions based on ethics, unless ethical behaviour is somehow picked up in the training data. Therefore, we must make AI ethical by design, but that is not easy.

Many of the ethical dilemmas arising from AI are difficult to solve, because the problems are so novel in a human context. Yet we all need to get used dealing with these ethical dilemmas at scale as we implement AI in our business operations.

To understand the unwieldy world of ethical AI, I recently spoke to James Brusseau who is a philosopher at Pace University, specialising in AI ethics.

His academic research explores the human experience of artificial intelligence in the areas of privacy, freedom, authenticity and personal identity and he works with organisations around the world to develop ethical AI applications.

In this episode of Leaders of Analytics, we discuss:

What AI ethics is and why it’s important
The most common dilemmas or challenges we face when it comes to AI ethics
Whether AI driven curation of information is a good thing or a bad thing
How we can develop a framework for dealing with ethical dilemmas at scale
How governments might regulate AI or introduce other incentives to achieve ethical AI by design
How leaders can get prepared for managing and governing the ethical implications of using AI in their operations, and much more.

There are so many ways to use AI technology in retail to improve customer experience, optimise supply chains and reduce waste.

Yet it seems to me that most innovations in the retail industry over the last 30 years have focused on automating labour-intensive tasks.

In my personal opinion, the retail customer experience has not improved markedly in my lifetime, and in some cases, it has gotten worse. Anyone who’s ever interacted with a self-checkout machine will know what I mean.

So, what is next for the retail industry and what can technology and data science do to improve efficiency and customer experience across the many disparate parts of retailing?

To answer these questions, I recently spoke to Shantha Mohan who is a true expert in the field.

Shantha is currently an Executive in Residence at the Integrated Innovation Institute at Carnegie Mellon University, where she co-delivers courses, contributes to curriculum design, and mentors students in their projects and practicums.

Shantha is also a co-founder and long-time executive of Retail Solutions Inc (RSi) where she ran the company’s worldwide product Development team that built the products & services which made the company a leader in retail analytics solutions used by consumer packaged goods companies and retailers across the globe.

She holds a PhD in Operations Management and a Bachelor of Engineering in Electronics and Communication Engineering.

In this episode of Leaders of Analytics, we discuss:

The applications of AI in retail with the most potential, for online and in-store shopping respectively
The differences between retail in developed and developing countries and how AI must be customised for different markets across the globe.
The typical consequences of items being out of stock and how can AI and other relevant technologies help combat out-of-stock problems.
Whether AI in retail will increase or diminish the ability for small retailers to compete, and much more.

If you dream of using analytics to optimise your customer interactions and squeeze additional value out of your existing operations, then is episode is for you!

Today, most large services businesses have established data science functions that churn out countless reports, dashboards, customer insights packs, machine learning models, forecasts and predictions.

With all this information to hand, you would hope that front-line operations are making data-driven decisions across the board. But alas, many of these same businesses struggle to turn their analytics into more than glossy PowerPoint packs that describe what could be done.

Often, this is because the technical implementation of data science solutions run into resource constraints or remain unsupported by IT departments.

So, how can we successfully make use of our analytical output in our front-line operations without spending eons creating overly complex systems that never quite deliver?

To answer this question, I recently spoke to Jason Tan who is an expert in operationalising data science solutions that deliver positive customer outcomes and real financial results.

Jason Is the managing director of consulting group Data Driven Analytics and an expert in optimising customer experience, pricing and long-term customer value.

In this episode of Leaders of Analytics, we discuss:

How to use analytics to optimise your customer interactions
How to identify the most valuable data science use cases in your organisation
How Jason has created successful data science solutions around legacy IT platforms
Whether you should buy off-the-shelf pricing software or build your own solution

AI and machine learning are seen by many as capabilities with enormous potential for unlocking digital personalisation and customer empathy at scale.

Organisations that get this right are disrupting industries and leaving old-school competitors broke. Just think of what global businesses like Netflix, Amazon and Facebook have been able to achieve with data-driven personalisation.

Yet, for many organisations, the promise of AI seems elusive or at least very hard to achieve. Many businesses are not realising the full potential of their stores of data, simply because they don’t know how.

To help us understand the potential of AI and ML for Customer Experience Management, I recently spoke to my friend and co-author of Demystifying AI for the Enterprise, Dr Kirk Borne.

Kirk is a truly unique individual who combines his incredible intelligence with a real passion for his chosen vocation. Having graduated with a PhD in Astrophysics, he spent 20 years working at NASA, before moving into the academic and corporate worlds.

He spent 12 years as Professor of Astrophysics and Computational Science, where he created the world’s first data science undergraduate degree. He since moved into data science consulting where he has been an executive for the past 6 years.

Kirk has a social media following of well over 300,000 which is a testament to the huge amount of value he creates through content creation and knowledge sharing.

In this episode of Leaders of Analytics, we discuss:

What data science, AI and machine learning can bring to digital and analogue customer experiences
The most valuable applications of AI for customer experience management
How AI can be used to amplify the abilities of front-line staff
Leading applications of AI-driven customer experience
The technical and organisational challenges that must be overcome to move up the analytics maturity curve
The importance of ModelOps in operationalising data science

Digitally connected humans like you and me are surrounded by a plethora of AI solutions that make our lives easier and more efficient. Just think about the algorithms driving Netflix and Youtube’s video recommendations or the facial recognition feature on your phone that saves you a few seconds every time you unlock it.

But for every useful AI solution, there are probably hundreds of solutions that don’t meet the functional, economic or ethical standards of their end users.

So, what’s the trick to building useful and impactful AI solutions that are also financially viable for those who create them?

Someone who can answer this question is Corey Quinn, who is the Chief Cloud Economist at The Duckbill Group and the founder of two podcasts called “Screaming in the Cloud” and “AWS Morning Brief”.

Corey combines an excellent sense of humour with a deep understanding of the cloud and everything that surrounds it, so he is definitely the right person to go to for an unfiltered view of the hype that surrounds a lot of AI solutions.

In this episode of Leaders of Analytics, we talk about:

Whether AI is all it’s made up to be or just a complex solution to our problems
Who’s benefiting from the AI hype
The role of cloud computing in AI and machine learning delivery
How to use cloud computing effectively when deploying AI solutions
How to create an impactful career by solving real business problems
Corey’s top 3 recommendations for AI success in the cloud

Digital transformation 2.0 is upon us! We have spent the last two decades digitising many products, services and processes to create digital experiences that are consistent, reliable and always on. That’s digital transformation 1.0 stuff.

The next decade will be all about creating data-driven personalisation at scale. Rather than treating everyone the same in our digital environment, we will increasingly be using customer data to tailor the customer experience to individual customer needs.

In this episode of Leaders of Analytics, we hear from Prashant Natarajan, Vice President of Strategy & Products at H2O.ai.

Prashant has spent more than 15 years helping organisations to successful digital transformations through his leadership roles in the sphere of technology and AI. He has made it his career to demystify AI and digital transformation for organisations and their staff across multiple industries and continents.

In this episode of Leaders of Analytics, we discuss:

what’s required to do digital transformation 2.0 successfully
how to create data-first organisations
how to use AI to take the robot out of humans
the future of automated machine learning
how organisations can ensure that their data science investments deliver actual business outcomes
our upcoming book, Demystifying AI for the Enterprise, which Prashant and I have co-authored alongside 5 other domain experts.

Data, networks and AI are eating the world and industries such as banking, insurance, utilities and telecommunications are changing rapidly as a result.

As an online product comparison portal and trusted third party to millions of consumers, Finder.com is well placed to be a huge winner from this trend. The company sits in the middle of many data-heavy industries that are about being disrupted by the data revolution.

The guest on this episode of Leaders of Analytics is Finder.com’s co-founder and CEO Fred Schebesta.

Fred is one of Australia's coolest and most successful entrepreneurs, now worth over half a billion dollars – all without funding.

He’s passionate about disruptive innovation and is a leader in the startup community where he shares his successes and knowledge as a mentor, international speaker, media commentator and author.

In this episode we talk about:

How Finder has grown from a two-man band to an international company.
How Finder is planning to use their recently received accreditation under CDR/Open Banking and what it means for Australian consumers and the financial services industry.
How Finder uses AI and machine learning to understand people’s finances and help them to better financial outcomes.
Why the company is betting big on cryptocurrencies and decentralised finance, including paying employees in Bitcoin.
How crypto will form part of the financial system of the future.
Fred’s new book, “Go Live! 10 Principles to Launch a Global Empire”.

Jamie is a data advocate with a strong belief in the transformative potential of data.

She is the founder and CEO of Open Data Australia and the regional director for FDATA Australasia and an advisor on digital identity to the United Nations Capital Development Fund.

Jamie is the go-to person for knowledge and insights on the topics of data privacy, governance, strategy, policy and regulation.

She has a vision for how data can be used to improve the lives and financial outcomes of everyday citizens

In this episode of Leaders of Analytics we discuss the huge potential for data innovation stemming from the Consumer Data Right and Open Banking, the hurdles that must be overcome by participating as well as who will be the winners and losers from the data sharing revolution.

In this episode you will find:

An overview of the Consumer Data Right and what it means to consumers and participating organisations?
How CDR differs from GDPR
How far participating organisations are in implementing the various components of Open Banking what should we expect to see in this space in the next 12-24 months
The most obvious use cases for CDR, and Open Banking in particular
The most important use cases that CDR/Open Banking participants should be focusing on
International examples of successful Open Banking based products and services
The hurdles currently limiting the use of the data sharing environment that CDR and Open Banking facilitates
How to generate consumer trust and excitement around CDR and Open Banking
What impacts CDR will have across the wider economy in the future
Who will be the future winners and losers from CDR, Open Banking and a broader Open Data regime