Radio DaTa: Recent Episodes
GetInData
We talk about data, cloud, analytics, and AI/ML/BI with different expert guests and different hosts, in different segment formats. Recorded by GetInData - a data management company founded by ex-Spotify data engineers who now build the cloud, AI, and data engineering solutions for other companies.
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Chris Tynan lives in London (the UK) and works as a Director of Data at DistroKid. Before joining DistroKid, Chris had been working at intersection of music, data and tech at Utopia Music and Spotify, as well as as a Lead Data Scientist in the UK Government Administration.
DistroKid is is the world’s largest music distributor to Spotify, Apple, Amazon, Tidal, TikTok, YouTube and all major streaming services. Most new music today is released through DistroKid and every day, millions of musicians rely on its products.
Topics that we talk about include:
- What DistroKid is, who uses it and how it works
- Data collected and analysed by DistroKid
- ML and AI in music distribution e.g. detecting bad actors
- Generative AI in the music industry e.g. deepfake voice
- Democratisation of music creation with Generative AI, mobile devices, social media
- Tech and ML/AI stack used and evaluated by DistroKid e.g. AWS, Redshift, dbt, Redash, Whisper, Amazon Rekognition
- Very interesting tools in the ML/AI landscape e.g. Hugging Face, Modal
- What makes working at DistroKid unique
The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, ML/AI, cloud, and MLOps projects and services at getindata.com.
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What's data management? How does it relate to data governance, data observability, and other similar terms? In this episode, we'll try to lay out what you should know about modern data management and how you and your organization could get involved in this topic.
Topics that the podcast includes:
- What Data Management is? How does it relate to data governance, data observability, and other similar terms?
- Why do we care about Data Management?
- What does it take to introduce data management in the organization?
- How do we get from an AS-IS situation to a desired well-managed data environment?
This podcast episode was recorded by Michał Rudko, Data Architect (GetInData | Part of Xebia).
Do you want to read more about the topics? Check "Data Democratization Through Data Management" White Paper written by Michał Rudko
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Agnieszka Bomersbach lives in Uppsala (Sweden) and works as a Staff Data Engineer at Pleo. Before joining Pleo, Agnieszka had been working at Acast and Skyscanner and she has graduated from the University of Edinburgh with Masters in Artificial Intelligence.
Pleo is a fintech scaleup from Denmark that builds is a cloud-based solution for managing company spending and automating expense reporting, using virtual and physical company cards. Today, Pleo is used and trusted by 25,000+ customers across Europe.
Topics that we talk about include:
- What Pleo is, who uses it and how it works
- How data is collected and utilized by Pleo
- Data analytics use-cases implemented at Pleo
- Pleo's data tech stack, Including GCP, BigQuery, Kafka, Metabase, and Looker
- Pleo's approach to Generative AI
- Focus for the upcoming months
- Evaluation criteria for deciding between open-source and proprietary technology
- Differences in working with data at Pleo, Acast, and Skyscanner
The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com.
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Kacper Łodzikowski lives in Poznań (Poland) and works as the Vice President of AI Learning Capabilities at Pearson. Kacper is also a researcher & lecturer in Artificial Intelligence at Adam Mickiewicz University in Poznań.
Pearson the world's leading learning company, serving customers in nearly 200 countries with digital content, assessments, qualifications, and data. The group's remit involves designing & building AI systems as well as providing technical & ethical leadership in application of AI for learning.
Topics that we talk about include:
- Data & AI functionalities provided by Pearson in their products
- Learning new (human) languages with Pearson and/or AI
- How AI changes the access to education and opens new opportunities worldwide
- The most important skills that one should focus on in the future
- What skills we should be teaching at schools & universities
- Disadvantages, negative consequences, risks of using AI in education
- Tech & data stack at Pearson
- Interesting future AI projects/challenges at Pearson
Links to topics that Kacper is referring to
- Skills Outlook report #1: the most in-demand skills from an employer’s perspective
- Skills Outlook report #2: how employees are preparing for a tech-focused world by building human skills
- Pearson’s leading AI textbooks
- About Pearson’s pioneering automated assessment and example data processing pipeline
- Pearson’s upcoming generative AI products
- Pearson 2023 Interim Results Presentation
The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com.
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Dainius Kniuksta lives in Copenhagen (Denmark) and works as a Artificial Intelligence Product Lead at Forecast. Dainius is a tech enthusiast with more than 15 years of digital product and platform management, strategy, development, team leadership, and PSA / log-tech / ad-tech focused Artificial Intelligence and Machine Learning experience in global companies like Maersk, Adform, and Sermo.
Forecast is the work intelligence company that is delivering an AI-native platform for profitable project & resource management. The platform automates busywork, surfaces best practices, predicts outcomes, guides projects to success, and most importantly empowers every team member to do their best work.
Topics that we talk about include:
- Introduction to Forecast and its integrated intelligence
- Data that is collected and analysed at Forecast e.g. projects, budgets, scope, time, people
- Data & AI-driven use-cases at Forecast e.g. reporting, warnings, similarity of tasks, work anomaly, burnout,
- Insights that can be taken from using Forecast related to people management, suitability (80% of accuracy currently), passion, career paths
- Tech, data & MLOps stack used to develop ML/AI models at Forecast e.g. NLP libraries, Google Bard, AWS, SageMaker, TensorFlow. Amplitude, home-grown solutions.
- Trends in people management, power of AI, AI vs. humans
- Using Forecast measure the adoption of AI tools in the tasks and projects
- Definition of the AI Product Lead role and his/her daily work at Forecast
The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com or contact us using hello@getindata.com.
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Ola Sars lives in Stockholm in Sweden and he is a successful serial music tech entrepreneur. He is the founder, CEO & chairman of Soundtrack Your Brand. Previously he started a number of successful business is the music/audio industry including e.g. Beats Music Beats by Dr. Dre.
Soundtrack Your Brand is a company that offers a cloud-based music streaming platform designed specifically for businesses (B2B). It provides licensed commercial music and analytics insights, plus curated playlists, customisable scheduling, and offline playback to help businesses create a unique and cohesive brand identity through music.
Topics that we talk about include:
- What Soundtrack Your Brand is, what the product is about, it’s value, who uses it, why and how
- The importance of data at Soundtrack Your Brand
- Data-driven use-cases implemented at Soundtrack Your Brand
- Complexity of building digital music streaming products
- Differences between B2B music streaming (e.g. Soundtrack Your Brand) vs. B2C music streaming (e.g. Apple, Spotify)
- Update of their road to profitability
- If and how data & AI helps in achieving profitability at Soundtrack Your Brand
- Current plans for investing in data & AI at Soundtrack Your Brand
- Generative AI in the B2B music streaming industry
- Interesting future trends for the next few years in the B2B music streaming industry
- Artists, music creators and their compensation in the B2B and B2C music industry
- Key metrics & dashboards that Ola looks at every day as the CEO of the company
The podcast was recorded by Adam Kawa (GetInData). Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com or contact us using hello@getindata.com.
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Jakub Janicki lives in Frankfurt (Germany) and works as Vice president | Big Data & Advanced Analytics at Commerzbank. He has over 15 years of experience in the financial industry, especially in using data & analytics at banks. Before joining Commerzbank in 2019, he had been working at mBank and Alior Bank. Topics that we talk about:
- How banks use data & analytics
- Types of data that banks analyze e.g. payments, clickstream, chatbot conversations
- Importance of personalized approach to every customer and its real-world examples
- Use of AI in banking industry e.g. Doc AI, Personalized AI assistants & advisors
- Leveraging AI to improve financial literacy, educate customers, help to build their financial portfolio
- Cutting-edge technologies at a banking industry e.g. cloud, metaverse
- Comparison between German and Polish banking sectors in the context of data, analytics, regulations and customer profiles.
- Why banking sector is so competitive and being a pioneer might not give you competitive advantages
The podcast was recorded by Adam Kawa (GetInData).
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Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com.
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Varun Bhatnagar works as a Lead Designer in DevOps and MLOps area in the Group Business Intelligence value stream at Swedbank. Before joining Swedbank, Varun had been working with designing effective solutions for cloud deployment and possesses in-depth knowledge of implementing core DevOps concepts such as containerization, virtualization, version control, cloud computing, database management & administration, load balancing, etc. by using a wide variety of technologies.
Swedbank is the largest bank in Sweden and the third-largest in the Nordic countries. Five years ago, the bank started looking at how to integrate and analyze data and use insights to improve decision-making. To assure a stronger position in the market, Swedbank migrated existing capabilities and application services within the Azure cloud. The advanced analytics platform i.e. Enterprise Analytics Platform (EAP) has made AI and ML programming languages available in one click. The platform is not just limited to one set of audience but instead provides capabilities that can cater to the needs of the whole organization when it comes to AI & Advanced Analytics. The implementation of machine learning operations (MLOps) using the platform has enabled shorter development cycles, which has resulted in shorter time-to-market.
Topics that we talked about:
- An overview of the solution - What is an Enterprise Analytics Platform (EAP)?
- Evolution of MLOps at Swedbank - How it all started and how has the solution evolved over time?
- Iterative development for ML models - How can one improve the iterative development process for ML models?
- The road ahead (after migration) and what's next?
- The secret of success - What has led to this successful migration?
- Key take-away points and the lessons learned from our ML cloud transformation journey and how can one start or improve in this area?
The podcast was recorded by Adam Kawa (CEO at GetInData).
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Find more about our data, analytics, cloud, and MLOps projects and services at getindata.com.
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Yetunde Data and Ivan Danov both live in London, work at QuantumBlack, and develop Kedro which is an open-source MLOps project. Yetunde works as a Director of Product Management, while Ivan works as a Senior Principal Machine Learning Engineer and the Engineering Director of Kedro.
QuantumBlack, a McKinsey company is a data science and advanced analytics company that works with customers from various industries. QuantumBlack was founded in 2009 and has its headquarters in London, United Kingdom. The company became a part of McKinsey & Company, a global management consulting firm, in 2015, and now operates as part of McKinsey's global analytics practice.
Topics that we talk about:
- What Kedro is and what problems it solves
- Reasons why different groups of data practitioners use Kedro
- Differences between companies that use and don't use Kedro
- Companies that use Kedro in production e.g. Telksomsel (Indonesia's largest telecom) - blog post
- Data and statistics that describe the adoption of Kedro
- When to use Kedro vs. Jupyter Notebooks
- Running Kedro everywhere, on all clouds and on-premise using various Kedro plugins e.g. VertexAI, Azure ML, SageMaker - blog post
- Using data, analytics, and community-driven insights in the product development of Kedro e.g. Kedro-Telemetry, Github, online training
- Current challenges, milestones, and focus areas for Kedro e.g. simpler configuration
- Trends in the MLOps landscape e.g. so many MLOps tools, LLMOPs
- Integration between Iguazio & QuantumBlack, and its plans for Kedro
The podcast was recorded by Adam Kawa (GetInData).
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Find more about GetInData at getindata.com.
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Ludwig Holmstrom works as a Product Analytics Director at Mentimeter. Before joining Mentimeter, he had been working with data & analytics for more than a decade at various companies such as Kry, Spotify, and Google.
Mentimeter is a Swedish company that builds an interactive audience engagement platform. The platform allows users to create and conduct real-time polls, quizzes, and surveys. It is often used in educational and business settings to engage audiences and gather feedback. With Mentimeter, presenters can create various types of interactive presentations, including multiple-choice questions, open-ended questions, and rating scales. Audience members can respond to these presentations in real-time using their smartphones or other internet-enabled devices. Mentimeter provides a wide range of customization options and analytics to help presenters make the most out of their presentations. The company's mission is to empower people to share their ideas, opinions, and knowledge in a more effective and engaging way.
Topics that we talk about:
- What audience engagement platform
- Analytics use-cases at Mentimeter e.g. real-time visualization, customer journey
- Autonomous teams at Mentimeter
- Analytics stack at Mentimeter e.g. AWS, Redshift, Looker
- KPIs and dashboards e.g. Pirate Metrics (AARRR), Viral loop, LTV (Customer lifetime value)
- Unique aspects of working with data at Mentimeter
The podcast was recorded by Adam Kawa (GetInData).
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Jonas Björkworks as the CTO at Acast. Before joining Acast around 5 years ago, Jonas had been working with data, analytics, and ML at Spotify, BizOne, and Ericsson.
Acast is a Swedish-founded podcast hosting and monetization platform that allows creators to distribute their podcasts to multiple podcast apps such as Spotify, Apple Podcasts, and Google Podcasts, and to monetize their content through advertising and listener support. Acast also provides analytics and data insights to help creators understand their audience and optimize their content, as well as tools for promoting and growing their podcasts. In addition, Acast offers targeted advertising solutions to brands and advertisers.
Topics that we talk about:
- Data collected and used by Acast
- Differences between measuring songs (e.g. on Spotify) and measuring podcasts (e.g. on Acast)
- Analytics use cases implemented at Acast
- Cloud-managed data tech stack at Acast e.g. AWS, Snowflake, Airflow, Python, Rust
- AI/ML in podcasting used today or tomorrow
- Trends and innovations in the podcasting industry
- The Acast's tech plans for 2023
- Interesting challenges when implementing data-specific projects in the podcasting industry
The podcast was recorded by Adam Kawa (GetInData).
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Liudmyla Taranenko works as the Head of Data Science at Metadata.io.
Metadata.io builds a product for B2B marketers that automates many manual and repetitive tasks. By taking care of tasks like running paid campaigns, personalizing web experiences, and optimizing everything for revenue, Metadata.io frees up time for marketers to focus on strategy, creativity, and driving revenue.
Topics that we talk about:
- Data sources collected and used by Metadata.io
- Data-driven features and product analytics at Metadata.io
- ML algorithms at Metadata.io e.g. Neural Networks
- Tech stack used at Metadata.io e.g. AWS, Databricks, (Py)Spark, MLflow
- Interesting data science challenges and edge cases at Metadata.io
- The future of marketing driven by data and AI
- The unique aspects of working with data science at Metadata.io
- The life cycle of data science projects at Metadata.io which ends with productization, UI, storytelling, and marketing
The podcast was recorded by Adam Kawa (GetInData).
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If you hear that your company should be data-driven, but you are not sure what does it mean in practice, in this episodes we share two stories of data driven companies. Both of them are the examples of data literate companies from different prospectives. In the fist story you can learn how the big tech company were allowed to detect problem and start solving the right one. The second one, how e-commerce company had prepared more effective promotion and increase revenue.This podcast episode was recorded by Adrian Dembek and Piotr Mencelewicz (GetInData | Part of Xebia).
Follow-up links:
- Data-driven survey - this survey helps you understand how data-drivenyour company is and identify data opportunities ahead
- Webinar: Data-Driven Fast Track: Introduction to data-drivenness
Henrik Feldtis the founder, CEO and CTO of Causiq. He previously worked as a cloud or system architect at companies such as VOI Technology or Tradera.
Causiq is a marketing analytics company. It builds a product that utilizes ML to give enterprises a comprehensive understanding of the efficiency of their marketing channels. This allows them to identify ROI for each marketing channel daily (or near real-time) and offers companies immediate insights into the efficiency of their marketing activities.
Topics that we talk about:
- What Causiq is, who uses it and why
- How data & ML are used at Causiq
- The importance of real-time ML in analyzing the efficiency of their marketing channels
- Tech stack used by Causiq (the mix of GCP and open-source such as Kafka, Flink, Hudi, dbt)
- Entrepreneurial advice for data engineers/architects who would like to launch their own product or company
The podcast was recorded by Adam Kawa (GetInData).
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Kevin Goldsmith works as a CTO at Anaconda, and he had previously worked in various roles at several companies e.g. CTO at Onfido and Avvo, as well as VP of Engineering at Spotify. He is also a Board Member and Advisor at several companies.
Anaconda is the most popular open-source distribution of the Python and R programming languages for data science that aims to simplify package management and deployment. Currently, ~30M practitioners from 235 countries and regions use Anaconda in their work.
Topics that we talk about:
- What Anaconda is, who uses it and why
- Data and analytics used internally by Anaconda
- The role and responsibilities of CTO at Anaconda
- SQL vs. Python in data science
- Hiring and layoffs in the tech industry
- An agile approach to data engineering and data science projects
The podcast was recorded by Adam Kawa (GetInData).
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Michał Wróbel works as a senior data engineer at RenoFi and has over 7 years of experience in data engineering and building data platforms.
RenoFi is a U.S.-based FinTech that uses the after-renovation value instead of your home's current value, enabling you to borrow the most money at the lowest rates.
Topics that we talk about:
- What RenoFi is, who uses it and why
- Data that is used at RenoFi
- Business use cases that are developed using this data (e.g. lead scoring)
- Modern Data Platform on top of Google Cloud Platform at RenoFi
- Building more (stuff) with less (people) at RenoFi
- Good decisions made by the CTO when launching the company
- Advanced ML/AI modes or real-time analytics - build or not to build at a startup?
- Plans for 2023 at RenoFi
The podcast was recorded by Adam Kawa (GetInData).
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Arunabh Singh lives in Stockholm in Sweden and he works as a director of data science at Willa.
Willa is a Sweden and U.S.-based FinTech that helps professional freelancers, influencers, and social media content creators get paid immediately by brands for their freelance work and paid collaborations. The company’s founders are former early members of Spotify’s growth team.
Topics that we talk about:
- What Willa is, who uses it, and why
- Data that is used at Willa and business use cases that are developed using this data
- The most important ML models implemented at Willa
- The ML(Ops) stack at Willa and a decision to build ML & Analytics capability very early
- The most important skills and competencies that data scientists should have these days
- The main trends and predictions for ML/AI for the next decades
- Plans for 2023 at Willa.
The podcast was recorded by Adam Kawa (GetInData).
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Alessandro Romano lives in Hamburg in Germany and he works as a data scientist at FREE NOW.
FREE NOW is Europe's largest multi-mobility app where you book a taxi, electric scooter, electric bike, and other vehicles.
Topics that we talk about:
- What FREE NOW is, who uses it and why Alessandro joined it as a data scientist
- Data, techniques, signals, and KPIs used to develop the dynamic pricing ML model for a real-time mobile app
- Working with stakeholders to understand changing priorities to adapt and optimize ML models
- Collecting the feedback from users in the interactive mobile app and running experiments and A/B tests
- The technology stack used by data scientists and ML engineers at FREE NOW
- The importance of using the right (sometimes simple or state-of-the-art) techniques to solve a particular problem rather than blindly following new fancy techniques and trends.
The podcast was recorded by Adam Kawa (GetInData).
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This time our episode title is "Future-Aware Data Engineer". It is the story of past and current inventions like Facebook by Mark Zuckerberg vs airplane by the Wright brothers. What is the Dunning-Krueger effect and what does it have in common with Wikipedia? Why did Jacek Kuroń not have to pay his phone bills?
We're going to look at the inventions through the lens of Yuval Noah Harari, Daniel Kahneman, and Slavoj Zizek. Seems like the perfect authors' trio for the ideal data-related holiday podcast.
This podcast episode was recorded by Paweł Leszczyński (GetInData).
PS. The podcast comes from one of our internal "Lunch & Learn" sessions at GetInData. You can find more topics of our "Lunch & Learn" session at GetIndata here.
Wouter de Bie lives in New Orleans and he has been working with big data for around 12 years! Wouter comes from the Netherlands, but he has spent most of his time working with data in Sweden and USA where he worked at Delta Projects, Spotify, The New York Times, and now as a Director Of Engineering at Datadog. Topics that we talk about:
- What Datadog is, who uses it, what data it collects, and how data is used in their product
- Multi-cloud developer experience at Datadog (technology stack, cloud providers, open-source)
- Future plans for the evolution of the data platform at Datadog
- Differences between Datadog and Spotify in the context of building the data platform, goals, and challenges
- Important patterns that one can notice when working with big data for 12 years
- Gaps and areas to watch for new tools/products in the data landscape
Thanks, Wouter!
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In this episode, we analyze changes and trends that resulted in the creation of so-called Modern Data Platforms. This includes e.g. adoption of best practices from the software development domain (e.g. dbt), the shift from ETL (Extract-Transform-Load) to ELT (Extract-Load-Transform) paradigm, serverless databases (e.g. Snowflake, BigQuery, Athena), public cloud, and support for SQL everywhere.
This podcast episode was recorded by Jakub Pieprzyk (GetInData).
Follow-up links:
- Modern Data Platform - the what's, why's and how's? Demystifying the buzzword
- Up & Running: data pipeline with BigQuery and dbt
- An example of a template to create a pipeline project with open-source GetInData Framework based on DBT (Github)
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Max Schultze lives in Berlin and he works as a data engineering manager at Zalando. Zalando is one of the largest European online retailers of shoes, fashion, and beauty. We talk with Max about how they use data and analytics at Zalando, and how their data platform has evolved during the last few years, however, the most important topic of our conversation is Data Mesh - what it is, what it is NOT, how it helps Zalando to become even more data-driven company, if, when & how to introduce it to your organization.
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I describe 5 trends in the data and AI landscape that we see in our daily work at GetInData and as a part of the data community (e.g. Big Data Tech Warsaw conference co-organizers).
- Retail becomes a very hot sector for AI/ML (plus new data sources, Metaverse, MLOps, Responsible AI)
- Modern Data Platforms (plus SQL, hiring, open-source, data engineering pipelines)
- Public Cloud (plus data residency, multi-cloud & cloud-agnostic approach)
- Data quality and data auditing
- Data access (data cataloging, data discovery, and data mesh).
Bonus: a few ideas on how to follow such trends.
This podcast episode was recorded by Adam Kawa (GetInData).
Follow-up articles and videos:
- EU Artificial Intelligence Act - where are we now,
- Auditing Data and Answering the lifelong question Is it the end of the day yet? - Simona Meriam
- Data Mesh as a proper way to organize the data world
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Sergiy Tkachuk lives in Warsaw in Poland and he works as a Data Science Manager at Reckitt. Reckitt is a British multinational consumer goods company that produces a number of well-known health, hygiene, and nutrition products such as Vanish, Calgon, and Air Wick. We talk with Sergey about how he and his company uses data, cloud, analytics, and AI/ML in the retail and FMCG industry. Data scientists at Reckitt have implemented many business use-cases such as basket analysis, attribution, reporting adverse events, and use the Azure cloud, Databricks, Power BI, and programming languages such as Python, Scala, R, and SQL.
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Simona works as a senior data engineer at Aidoc. Aidoc is an Israeli healthcaretechnology company that develops AI solutions to analyze medical images so that physicians can expedite patient treatment and enhance efficiencies. We talk with Simona about two topics. The first of them is working as a data engineer with complex & unstructured data such as medical images at Aidoc and the second is data auditing. Before joining Aidoc, Simona had been working at Nielsen for almost 5 years where she had built, among others, an in-house data auditing solution for tracking a large volume of text data (used technologies include Kafka, Avro, Spark, AWS Lambda, and SQL).
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Viktoria works as a senior data engineer at Shopify. Shopify is one of the most well-known e-commerce companies and it is a very early adopter of big data & cloud technologies. We talk with Viktora about how her team ingests data at Shopify using a mix of open-source and cloud-native technologies such as Apache Iceberg, Debezium, Kafka, and GCP.
Our GDPR privacy policy was updated on August 8, 2022. Visit acast.com/privacy for more information.