The Data Playbook Podcast
🎙️ The Data Playbook is a podcast where we aim to build a playbook for data leaders. We do that through a series of interviews with other data leaders, data practitioners and data experts. In each episode, we break down real-world data challenges: from building modern architectures and embracing Data Mesh to navigating cloud sovereignty, we help you make smarter decisions one play at a time.
All Episodes
S2 E3. How imec scales research with data platforms: governance, workbenches, and adoption - The Data Playbook Podcast with Kris Peeters & Wim Vancuyck
<p>In this episode of <em>The Data Playbook</em>, we go inside <strong>imec</strong>, one of the world’s leading <strong>semiconductor research institutes</strong>, to explore how they scale data governance, self-service, and innovation in one of the most data-intensive environments on Earth.</p> <p>Our guest, <strong>Wim Vancuyck</strong>, <strong>Manager of ICT for Data & Research Enablement</strong>, leads imec’s data strategy - bridging IT, researchers, and business to accelerate R&D through digital solutions. Wim’s mission: make <strong>imec</strong> a <strong>data-driven research organisation</strong> that turns raw measurements into insights and intellectual property faster and more securely.</p> <p>Wim explains how imec built a <strong>research data platform</strong> that empowers thousands of scientists through:</p> <ul> <li><strong>Purpose-based access control</strong>, linking people, platforms, and data assets</li> <li>Four <strong>self-service workbenches</strong> for Power BI, Data Engineering, Data Science & AI, and Application Development</li> <li>A clear platform vision built on <strong>efficiency, scalability, and reliability</strong></li> <li>A governance model that supports both compliance and creativity</li> <li>A pragmatic stance on <strong>shadow IT:</strong> embrace, standardise, and professionalize it</li> <li>A bottom-up adoption strategy driven by <strong>early adopters and community engagement</strong></li> </ul> <p>He also discusses his evolution from technical architect to data leader, and what it takes to manage change in a 5,000-person R&D organisation, balancing technical depth with people leadership.</p> <p>🎙️ <strong>Guest:</strong> Wim <strong>Vancuyck</strong> - Manager ICT, Data & Research Enablement, imec</p> <p>🌐 More at: <a href="https://www.dataminded.com/" rel="nofollow">www.dataminded.com</a></p> <p>#DataPlaybook #imec #SemiconductorR&D #DataGovernance #PurposeBasedAccessControl #DataMesh #PlatformEngineering #SelfServiceAnalytics #CIO #CDO #DataLeadership #ResearchDataPlatform #DataStrategy</p>The Data Challenge behind the Einstein Telescope - The Data Playbook Podcast with Kris Peeters & Tjonnie Li
<p>What does it take to listen to the universe?</p> <p>In this episode of <strong>The Data Playbook</strong>, Kris Peeters talks with <strong>Tjonnie Li</strong>, Professor at <strong>KU Leuven</strong>, about gravitational waves, black hole collisions, and the massive data challenge behind the <strong>Einstein Telescope</strong>.</p> <p>They explore how modern science is becoming deeply data-driven, why the next generation of research infrastructure will need to operate like a <strong>science factory</strong>, and how AI, automation, and large-scale compute could become essential for turning petabytes of raw data into scientific discovery.</p> <p>This episode covers:</p> <ul> <li>what gravitational waves are and why they matter</li> <li>how black hole collisions are measured</li> <li>why the Einstein Telescope could transform European science</li> <li>the data, compute, and storage challenge behind next-gen physics</li> <li>what academia can learn from industry about automation and orchestration</li> <li>how AI agents could support future scientific discovery</li> </ul> <p>👉 Subscribe for more episodes: <a href="https://www.youtube.com/@Dataminded" rel="nofollow">https://www.youtube.com/@Dataminded</a></p> <p>👉 Watch on YouTube: <a href="https://youtu.be/aBbykwnsmpI" rel="nofollow">https://youtu.be/aBbykwnsmpI</a></p> <p>👉 Explore more content & insights: <a href="https://dataminded.com" rel="nofollow">https://dataminded.com</a></p> <p>👉 Follow Dataminded on LinkedIn: <a href="https://www.linkedin.com/company/dataminded" rel="nofollow">https://www.linkedin.com/company/dataminded</a></p> <p>#DataEngineering #AI #GravitationalWaves #EinsteinTelescope #BigData #ScientificComputing #ResearchInfrastructure #DataPlaybook</p> <p><br></p> <p>Chapters:</p> <p><strong>00:00</strong> Intro: <strong>Tjonnie Li</strong> joins The Data Playbook</p> <p><strong>02:08</strong> What gravitational waves are - in plain English</p> <p><strong>05:19</strong> Why science is becoming data-driven</p> <p><strong>11:28</strong> How we measure black hole collisions today</p> <p><strong>15:50</strong> The Einstein Telescope: ambition, timeline, and European bid</p> <p><strong>19:44</strong> The data infrastructure challenge: from terabytes to petabytes</p> <p><strong>30:56</strong> AI, automation, and the idea of a “science factory”</p> <p><strong>38:50</strong> Why this matters for Europe, innovation, and society</p>Scaling Data in Aviation: Inside Brussels Airlines’ Data Strategy - The Data Playbook Podcast with Kris Peeters & Tom Holsteens
<p><strong>How do you transform a broken data landscape into a scalable, self-service data platform?</strong></p> <p>In this episode of <em>The Data Playbook</em>, Kris Peeters sits down with Tom Holsteens to unpack how Brussels Airlines rebuilt their data foundation from the ground up.</p> <p>Coming out of the pandemic, the organisation faced a classic problem:</p> <p>👉 A “spaghetti” data warehouse</p> <p>👉 No ownership of data assets</p> <p>👉 A central team becoming the bottleneck</p> <p>What followed was a multi-year transformation focused on:</p> <ul> <li>Building a <strong>modern cloud data platform</strong></li> <li>Moving to a <strong>data product architecture</strong></li> <li>Enabling <strong>self-service analytics across teams</strong></li> <li>Balancing <strong>central governance with decentral ownership</strong></li> <li>Leveraging <strong>AI tools to empower non-technical users</strong></li> </ul> <p>💡 You’ll learn:</p> <ul> <li>Why most data platforms fail (and how to fix them)</li> <li>How to introduce <strong>data ownership in business teams</strong></li> <li>The real difference between <strong>controlling vs. BI</strong></li> <li>How to reduce bottlenecks with <strong>hub-and-spoke models</strong></li> <li>A real use case: <strong>cutting food waste by 30% with data</strong></li> <li>Why <strong>perfect data quality is a myth</strong></li> </ul> <p>This is a must-watch for data leaders, engineers, and anyone scaling data in complex organisations.</p> <p>👉 Subscribe for more episodes:</p> <p><a href="https://www.youtube.com/@Dataminded" rel="nofollow">https://www.youtube.com/@Dataminded</a></p> <p>👉 Listen on Spotify:</p> <p><a href="https://open.spotify.com/show/your-podcast-link" rel="nofollow">https://open.spotify.com/show/your-podcast-link</a></p> <p>👉 Explore more content & insights:</p> <p><a href="https://dataminded.com" rel="nofollow">https://dataminded.com</a></p> <p>Struggling with data bottlenecks, unclear ownership, or slow delivery?</p> <p>👉 Explore our <strong>Data Product Workshop</strong>:</p> <p><a href="https://www.dataminded.com/what-we-do/data-product-workshop" rel="nofollow">https://www.dataminded.com/what-we-do/data-product-workshop</a></p> <p>Turn your data landscape into a <strong>business accelerator</strong> with a shared framework, clear ownership, and hands-on guidance in just one day.</p> <p><strong>Chapters </strong></p> <p><strong>00:00</strong> Introduction & Brussels Airlines context<br><strong>02:30</strong> What is controlling vs. business intelligence?<br><strong>06:00</strong> The problem: “spaghetti” data warehouse & bottlenecks<br><strong>12:30</strong> The transformation: platform, operating model & group strategy<br><strong>19:00</strong> Hub-and-spoke model & self-service analytics<br><strong>27:30</strong> Data products & the “restaurant” analogy<br><strong>35:30</strong> AI, data analysts & scaling data adoption<br><strong>43:30</strong> Real impact: reducing waste & driving business value</p>Machine Learning in Energy: Forecasting, MLOps, and Business Impact - The Data Playbook Podcast with Kris Peeters & Jean-Michel Begon
<p>How do you move machine learning from notebook experiments to production in a real business environment?</p> <p>In this episode of <strong>The Data Playbook Podcast</strong>, Kris Peeters sits down with <strong>Jean-Michel Begon</strong>, Senior Machine Learning Engineer at <strong>Luminus</strong>, to explore how machine learning models are built and operationalized inside an energy company.</p> <p>They discuss <strong>electricity demand forecasting</strong>, the <strong>machine learning lifecycle</strong>, model experimentation, industrialisation, monitoring, collaboration with IT, and the role of <strong>GenAI and LLMs</strong> in modern ML teams.</p> <p>You’ll hear practical lessons on:</p> <ul> <li>production machine learning</li> <li>ML team structure</li> <li>forecasting model development</li> <li>data pipelines and platform support</li> <li>model monitoring and performance review</li> <li>balancing business value with technical rigor</li> </ul> <p>Explore the full podcast series: <a href="https://youtube.com/playlist?list=PLJ_da7qdfL83BFGdNF0CIFB9ygsYPH2Ai&si=4IKtFqBcZhUSg77r" rel="nofollow">The Data Playbook Playlist</a>Discover more podcasts, blogs, and webinars: <a href="https://www.dataminded.com/resources" rel="nofollow">Dataminded Resources</a>Visit the Dataminded website: <a href="https://www.dataminded.com/" rel="nofollow">https://www.dataminded.com/</a></p>