The Data Playbook Podcast

What does it take to listen to the universe?In this episode of The Data Playbook, Kris Peeters talks with Tjonnie Li, Professor at KU Leuven, about gravitational waves, black hole collisions, and the massive data challenge behind the Einstein Telescope.They explore how modern science is becoming deeply data-driven, why the next generation of research infrastructure will need to operate like a science factory, and how AI, automation, and large-scale compute could become essential for turning petabytes of raw data into scientific discovery.This episode covers:what gravitational waves are and why they matterhow black hole collisions are measuredwhy the Einstein Telescope could transform European sciencethe data, compute, and storage challenge behind next-gen physicswhat academia can learn from industry about automation and orchestrationhow AI agents could support future scientific discovery👉 Subscribe for more episodes: https://www.youtube.com/@Dataminded👉 Watch on YouTube: https://youtu.be/aBbykwnsmpI👉 Explore more content & insights: https://dataminded.com👉 Follow Dataminded on LinkedIn: https://www.linkedin.com/company/dataminded#DataEngineering #AI #GravitationalWaves #EinsteinTelescope #BigData #ScientificComputing #ResearchInfrastructure #DataPlaybookChapters:00:00 Intro: Tjonnie Li joins The Data Playbook02:08 What gravitational waves are - in plain English05:19 Why science is becoming data-driven11:28 How we measure black hole collisions today15:50 The Einstein Telescope: ambition, timeline, and European bid19:44 The data infrastructure challenge: from terabytes to petabytes30:56 AI, automation, and the idea of a “science factory”38:50 Why this matters for Europe, innovation, and society

How do you transform a broken data landscape into a scalable, self-service data platform?In this episode of The Data Playbook, Kris Peeters sits down with Tom Holsteens to unpack how Brussels Airlines rebuilt their data foundation from the ground up.Coming out of the pandemic, the organisation faced a classic problem:👉 A “spaghetti” data warehouse👉 No ownership of data assets👉 A central team becoming the bottleneckWhat followed was a multi-year transformation focused on:Building a modern cloud data platformMoving to a data product architectureEnabling self-service analytics across teamsBalancing central governance with decentral ownershipLeveraging AI tools to empower non-technical users💡 You’ll learn:Why most data platforms fail (and how to fix them)How to introduce data ownership in business teamsThe real difference between controlling vs. BIHow to reduce bottlenecks with hub-and-spoke modelsA real use case: cutting food waste by 30% with dataWhy perfect data quality is a mythThis is a must-watch for data leaders, engineers, and anyone scaling data in complex organisations.👉 Subscribe for more episodes:https://www.youtube.com/@Dataminded👉 Listen on Spotify:https://open.spotify.com/show/your-podcast-link👉 Explore more content & insights:https://dataminded.comStruggling with data bottlenecks, unclear ownership, or slow delivery?👉 Explore our Data Product Workshop:https://www.dataminded.com/what-we-do/data-product-workshopTurn your data landscape into a business accelerator with a shared framework, clear ownership, and hands-on guidance in just one day.Chapters 00:00 Introduction & Brussels Airlines context02:30 What is controlling vs. business intelligence?06:00 The problem: “spaghetti” data warehouse & bottlenecks12:30 The transformation: platform, operating model & group strategy19:00 Hub-and-spoke model & self-service analytics27:30 Data products & the “restaurant” analogy35:30 AI, data analysts & scaling data adoption43:30 Real impact: reducing waste & driving business value

How do you move machine learning from notebook experiments to production in a real business environment?In this episode of The Data Playbook Podcast, Kris Peeters sits down with Jean-Michel Begon, Senior Machine Learning Engineer at Luminus, to explore how machine learning models are built and operationalized inside an energy company.They discuss electricity demand forecasting, the machine learning lifecycle, model experimentation, industrialisation, monitoring, collaboration with IT, and the role of GenAI and LLMs in modern ML teams.You’ll hear practical lessons on:production machine learningML team structureforecasting model developmentdata pipelines and platform supportmodel monitoring and performance reviewbalancing business value with technical rigorExplore the full podcast series: The Data Playbook PlaylistDiscover more podcasts, blogs, and webinars: Dataminded ResourcesVisit the Dataminded website: https://www.dataminded.com/

In this season finale of The Data Playbook Podcast by Dataminded, the tables turn: Kris Peeters (Host & Founder of Dataminded) is interviewed by Pascal Brokmeier (guest from the Episode 2 and former colleague).Kris shares the real story behind 11 years of building Dataminded - from the stress of having zero customers, to landing the first project, to scaling from a small team to a company with a leadership layer. We dive deep into what makes an engineering-first culture work: autonomy + responsibility, raising (and protecting) the hiring bar, learning from mistakes, and why timeless engineering practices (Git, CI/CD, testing, monitoring) still matter, no matter the tech hype cycle.If you’re a data leader, data engineer, engineering manager, or founder, this episode is a practical playbook on building a company (and a culture) that can survive and scale.✅ Subscribe and follow Dataminded for more episodes, deep dives, and real-world data engineering stories. https://www.youtube.com/@Dataminded✅ Explore The Data Playbook Podcast archive for more conversations on data platforms, data products, AI, and cloud decisions. https://www.dataminded.com/resources/podcast✅ Want to work with us? Check our open roles or reach out directly.Open vacancies: https://www.dataminded.com/about/join-usOr email: [email protected]:00:06 - 11 Years of Dataminded: Why This Story Matters01:54 - Why Kris Founded Dataminded (Engineers First)04:12 - From Zero Clients to the First Big Win07:53 - First Hires & How Culture Was Born11:14 - Git, CI/CD & Why Engineering Discipline Wins15:59 - Growing from 6 to 20: Chaos to Structure23:30 - Autonomy, Trust & Professional Culture35:13 - COVID, Overhead & the Push to 50 People41:13 - How Dataminded Keeps the Hiring Bar High55:59 - Germany, The Netherlands & What’s Next

In this episode of The Data Playbook, we sit down with Dr. Ruth Janning, Head of Data Science & AI at OBI, to talk about what actually drives AI impact in real organisations.We break down:Why GenAI hype leads teams in the wrong directionHow to choose between ML, GenAI and agentic AIReal-world retail AI use cases (recommendations, assortment, automation)How a 10-person team delivers outsized business valueAI governance, self-service, templates & AI ambassadors🎧 Listen to more episodes of The Data Playbook for real-world stories on data platforms, GenAI, data products and cloud independence from Europe’s leading data practitioners and leaders.🌐 More at https://www.dataminded.com/resources and subscribe to our Spotify channel. Watch the full episode on YouTube: https://youtu.be/hzI9VizGyHM

In this episode of The Data Playbook, Jelena Grujic (Dataminded) explains why the divide between data scientists and data engineers still exists and how to finally break it.We dive into real-world conflicts around unit tests, notebooks, production data access, documentation, and overengineered solutions. Jelena shares pragmatic alternatives like data testing, functional pipelines, and purpose-based access that actually work in production.A must-listen for data leaders and practitioners who want fewer debates and better data products.🎧 Topics include data testing, notebooks, production data, functional programming, and team collaboration.🎧 Listen to more episodes of The Data Playbook for real-world stories on data platforms, GenAI, data products and cloud independence from Europe’s leading data practitioners and leaders.🌐 More at https://www.dataminded.com/resources#DataScience #DataEngineering #DataLeadership #DataTeams #DataPlatform #AnalyticsEngineering #DataInProduction #MachineLearning #ModernDataStack #TheDataPlaybook

Most data leaders know the statistic: the majority of big data initiatives never deliver the value they promised. In this episode, Kris sits down with Frederic Vanderveken from Dataminded to unpack a practical framework to choose and validate the right data use cases.We cover:Why so many data initiatives fail before they even startHow to anchor your work in business strategy, not technologyHow to run problem discovery interviews that surface real headaches, not minor annoyancesPrioritising solutions using five lenses: customer, growth, money, pragmatic feasibility and differentiatorsBuilding a quantifiable business case and defining success upfrontIf you’re a data leader or product owner deciding where to place your next big bet, this episode gives you a structured way to reduce risk and ship data solutions that actually move the needle.Follow The Data Playbook for more episodes on data platforms, data products and making AI useful in real life.🌐 More at ⁠www.dataminded.com⁠ and subscribe to our channel.⏱️ Chapters:00:00 Introduction to Data Solutions Framework09:03 Effective Problem Discovery Techniques17:53 Mapping Customer Journeys26:39 Collaborative Solution Brainstorming32:27 Testing Solutions and Integration44:39 Final Thoughts and Key Takeaways

Kris Peeters sits down with Amaury Anciaux, founder of River Solutions, to tackle a painful reality for data leaders: critical decisions still depend on fragile Excel models.They explore why Excel won’t disappear, how River turns spreadsheets into visual, explainable and reliable decision models, and what happens when you bring data quality checks, testing and documentation into the analyst workflow.Topics include:Why 99% of models in organisations are still built in ExcelSilent errors, risk, and the real cost of debugging formulasVisual flow-based modelling and model maps inside ExcelBuilt-in checks for missing data, duplicates and broken lookupsHow AI copilots helped build River, and why AI won’t replace transparent modelsThe evolving role of analysts and managers in data-driven decisions🎧 Listen to more episodes of The Data Playbook for real-world stories on data platforms, GenAI, data products and cloud independence from Europe’s leading data practitioners and leaders.🌐 More at https://www.dataminded.com/resources Chapters: 00:00 – Intro & episode setup00:45 – Amaury’s background & consulting career02:00 – The hidden reality of Excel decision models04:00 – Why “just get it out of Excel” doesn’t scale05:10 – What River Solutions does in Excel06:40 – Visual model maps for explainable models08:40 – Removing formulas & adding data quality checks10:50 – Why Excel errors are so risky for big decisions13:15 – Who River is for: analysts, Excel gurus & managers16:05 – Why Amaury started River now & building with Copilot19:00 – Will AI copilots replace River and Excel modelling?22:51 – How River works as an Excel add-in (UX & interactivity)26:25 – How River changes the analyst role (less debugging, more thinking)28:10 – Roadmap: community, cloud, AI & connecting to data warehouses31:14 – Biggest lesson learned: software is easy, change is hard

What happens when a bank decides that AI and IP are so strategic they must be built in-house - then actually follows through for more than a decade?In this episode of The Data Playbook, Dr. Barak Chizi, Chief Data & Analytics Officer at KBC Group, joins Kris Peeters to reveal how KBC built one of Europe’s most mature AI organisations and what it took to bring Kate, their AI assistant, to life, and keep her evolving for 5 years.You’ll hear how KBC:Grew from early machine learning to 2,000+ AI use cases in productionDeveloped an AI-driven anti-money laundering platform and commercialised it for other banksScaled Kate, now celebrating 5 years and upgraded with GPT.Uses the U-model to govern AI safely from idea to productionKeeps ROI at the centre of every AI projectStays vendor-independent while still leveraging hyperscaler LLMsBuilds diverse, high-calibre AI teams with a rigorous recruitment approachExplores soft logic and modelling customer intent as the next frontier of financial AIIf you want to understand how to turn AI from experiments into a true competitive advantage, this conversation is your playbook.🌐 More at www.dataminded.com and subscribe to our channel.Show notes:The Foundation of Soft Logic👉 https://link.springer.com/book/10.1007/978-3-031-58233-2Dan Ariely – Predictably Irrational👉 https://www.amazon.com/Predictably-Irrational-Revised-Expanded-Decisions/dp/0061353248/⏱️ Chapters00:00 – Intro to The Data Playbook & today’s guest01:15 – Barak’s backstory: 25 years in AI & high-dimensional data03:02 – What a CDAO does at KBC & enabling 24/7 AI-assisted service04:55 – Towards continuous, machine-supported customer journeys06:37 – The U-Model: KBC’s framework for data & AI projects08:35 – Flagship AI products, finite project lifecycle & retraining10:07 – Prioritising AI use cases across 5 countries12:31 – ROI mindset, conservative risk culture & data as an asset14:21 – Why KBC keeps AI in-house & limits external consultants18:17 – Beyond data warehouses: from reporting to prediction22:21 – AI-driven AML platform & the creation of SKY25:30 – Patents, AI IP and KBC’s competitive positioning27:25 – Generative AI at KBC since 2018 & early transformer experiments29:11 – Pragmatic tech choices: LLMs vs ML vs simple automation31:42 – Avoiding GenAI hype and focusing on customer value33:03 – Why KBC built Kate: 24/7 banking & impatient customers35:28 – From FAQ bot to execution engine: Kate’s end-to-end capabilities37:07 – Customer reactions, branches vs digital & Kate’s 2026 roadmap39:24 – Multi-LLM strategy, vendor independence & design partnerships40:44 – Inside Kate’s architecture: NLU, open source & KBC-built layers42:37 – Proactive AI: timing, context and personalised offers44:51 – Soft logic, consciousness & modelling customer intent49:19 – Building a diverse, 24-nationality AI team at KBC51:37 – Recruitment process, tests & how candidates are evaluated55:21 – What KBC looks for in modern data scientists57:15 – Lessons after 10 years at KBC & book recommendation

EU clouds without the hype. Niels Claeys (Partner & Lead Data Engineer at Dataminded, and our technical hiring lead) breaks down data sovereignty vs. Cloud Act, GDPR realities, and a portable, Kubernetes-first stack with Iceberg, Trino, and Airflow. We compare Scaleway, OVH, Exoscale, UpCloud, look at cost drivers, encryption/KMS, egress policies, and how to avoid vendor lock-in plus when best-of-breed beats all-in-one and why “keep it simple” still wins.What you’ll learn:When EU clouds make more sense than hyperscalers (and when they don’t)Designing a portable platform: Terraform/Tofu for infra, Argo CD for appsTable formats 101: why Apache Iceberg over plain Parquet/CSVQuery layer choices: Trino for open SQL across object storage & DBsOrchestration in practice: Airflow patterns, dependencies, SLAsSecurity & governance: OPA for fine-grained policies, IAM, catalogsCost & ops: egress, managed services gaps, version lag, troubleshootingTeam skills: what to hire for, and the “hard questions” Niels asks in interviews🌐 More at ⁠www.dataminded.com⁠ — and subscribe!Chapters00:00 Intro & why EU clouds now04:40 Compliance & legal: GDPR, Cloud Act, sovereignty11:55 Platform blueprint: Kubernetes + Iceberg + Trino + Airflow20:30 Catalogs, OPA, IAM & access control27:10 EU providers deep dive: Scaleway, OVH, Exoscale, UpCloud36:20 Cost, encryption/KMS, egress & performance43:10 Best-of-breed vs all-in-one (and glue work)51:00 Getting started: IaC, Argo CD, day-2 ops56:40 Hiring: interview signals & practical takeawaysKeywordsEU cloud, European cloud providers, data sovereignty, GDPR, Cloud Act, Kubernetes data platform, Apache Iceberg, Trino, Airflow, vendor lock-in, OPA, Argo CD, Terraform, Exoscale, Scaleway, OVH, UpCloud

Belfius Insurance’s Head of Data & AI, Hannes Heylen shares how his team scaled GenAI - from a fraud detection flywheel to “Nestor,” a claims copilot that speeds summaries, completeness and coverage checks. We unpack AI agents in the claims flow, build-vs-buy decisions, and why content/data governance drives LLM quality. Plus: a pragmatic delivery mantra - make it work, then right, then cheap - for CIOs, CDOs and Heads of Data.What you’ll learnHow to pick first AI cases that prove €ROI (fraud models)Designing a claims copilot: summarization, completeness & coverage checksWhere AI agents fit (GenAI + ML + humans) across the claims flowBuild vs. buy in 2025: foundation models, vendor flexibility, cost controlContent/data governance as the make-or-break for LLM apps“First make it work, then right, then cheap”: an AI operating model for CIO/CDOGuest: Hannes Heylen, Head of Data & AI, Belfius Insurance🌐 More at ⁠www.dataminded.com⁠Chapters:00:00 Why AI now in financial services06:30 GenAI’s impact on text-heavy insurance processes18:40 AI agents across claims31:00 Governance > model tweaks38:00 Fraud detection: the € case41:30 Claims copilot (“Nestor”) & lab-to-prod55:00 Lessons for CIOs/CDOsTopics: ROI-first use cases • Claims automation • AI agents (GenAI + ML + human-in-the-loop) • Governance • Vendor flexibility & costs

In this episode of The Data Playbook, we go inside imec, one of the world’s leading semiconductor research institutes, to explore how they scale data governance, self-service, and innovation in one of the most data-intensive environments on Earth.Our guest, Wim Vancuyck, Manager of ICT for Data & Research Enablement, leads imec’s data strategy - bridging IT, researchers, and business to accelerate R&D through digital solutions. Wim’s mission: make imec a data-driven research organisation that turns raw measurements into insights and intellectual property faster and more securely.Wim explains how imec built a research data platform that empowers thousands of scientists through:Purpose-based access control, linking people, platforms, and data assetsFour self-service workbenches for Power BI, Data Engineering, Data Science & AI, and Application DevelopmentA clear platform vision built on efficiency, scalability, and reliabilityA governance model that supports both compliance and creativityA pragmatic stance on shadow IT: embrace, standardise, and professionalize itA bottom-up adoption strategy driven by early adopters and community engagementHe 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.🎙️ Guest: Wim Vancuyck - Manager ICT, Data & Research Enablement, imec🌐 More at: www.dataminded.com#DataPlaybook #imec #SemiconductorR&D #DataGovernance #PurposeBasedAccessControl #DataMesh #PlatformEngineering #SelfServiceAnalytics #CIO #CDO #DataLeadership #ResearchDataPlatform #DataStrategy

How do you turn governance from a bottleneck into a business accelerator - in a bank?In this episode of The Data Playbook, host Kris Peeters talks with Jan Mark Pleijsant, Senior Data Strategy & Governance Advisor at ABN AMRO Bank, about their move from 300+ dispersed data owners to 15 clear, business-aligned data domains and what it took to make federated governance work in a highly regulated environment.You’ll learn:Why “governance police” fails and how a federated model balances autonomy with oversight.How ABN AMRO defined domains by the nature of data (customer, loans, payments) rather than shifting business units driving stability across reorganisations.What “governance by design” looks like in practice (policies, standards, data lineage, quality, clear ownership).The evolution from golden (source-aligned) datasets to consumer-ready data products (“pre-packaged salads”) that speed time-to-insight.Why executive sponsorship, domain maturity, and delivery discipline are the real success factors.How to measure domain maturity, prioritize critical reports (e.g., regulatory & exec dashboards), and avoid endless debates over definitions by making differences explicit.Who should listen: CIOs, CDOs, Heads of Data, and Data Leaders building scalable, compliant data platforms in complex organizations - especially in financial services.Chapters:00:00 Intro & context (POA Summit)03:12 Why banks need strong data governance07:45 Federation vs. centralization (and pitfalls)13:10 From 300 owners to 15 domains19:40 Designing domains that don’t break with reorgs26:05 Data products vs. datasets—what changed33:20 Governance by design: policy → product40:05 Measuring maturity & building momentum47:30 Risks, success factors, and the next 24 months🌐 More at https://www.dataminded.com/#DataGovernance #FederatedGovernance #DataDomains #DataProducts #DataLeadership #BankingData #ABNAMRO #DataStrategy

What does it take to scale data products across an organization? In this episode of The Data Playbook, we sit down with Simon Harrer, CEO and Co-Founder of Entropy Data, recorded live at the POA Summit 2025 in Stuttgart.Simon unpacks his journey from developer to founder, the creation of Data Mesh Manager, and why data contracts are becoming the backbone of modern data governance. We dive deep into:The evolution from consulting to product-based data companiesHow data products and contracts drive interoperabilityWhy AI and MCPs will redefine how data is shared and governedThe future of Data Mesh and the rise of data marketplacesA conversation packed with real-world lessons for Data Leaders, CIOs, and CDOs driving digital transformation.🌐 More at www.dataminded.com🔗 Resources from the episode:Entropy DataData Mesh ManagerData Contract CLIData Product MCPEntropy Spin-off Story

In this episode of the Data Playbook podcast, we dive into how publiq, the organization behind Belgium’s largest cultural event database, is building RADAR, an AI-powered framework that enriches and structures event data at scale.Host Kris Peeters is joined by Sven Houtmeyers (CTO) and Elia Van Wolputte (Data Scientist) from Publiq, who share how their team uses LLMs, semantic parsing, and linked data to improve search, recommendations, and user experience, all while respecting publiq values like privacy, transparency, and digital inclusion.Topics covered:Why unstructured data is a challenge for cultural event discoveryHow RADAR leverages LLMs for smarter enrichment and entity resolutionFrom batch jobs to live services: Scaling across GCP and AWSDesigning ethical AI in the public sector: avoiding filter bubbles and over-personalizationThis is a behind-the-scenes look at how public organizations can use modern AI tools, not to manipulate users, but to empower them.🌐 More at ⁠www.dataminded.com

🎙️ In this episode, host Kris Peeters talks with Jelle De Vleminck, consultant at Dataminded, about what it really takes to build a data platform that people actually want to use.Together, they explore:What separates a platform from just “a bunch of tools”How to reduce cognitive load for developersThe 5 biggest mistakes in platform designWhy adoption matters more than featuresHow data contracts, products, and cloud IDEs improve usabilityWhy enabling your users beats controlling themIf you’re building internal tooling or scaling data across teams, this episode is packed with practical insight.🌐 More at www.dataminded.com

In this episode of The Data Playbook, we explore what it really takes to turn AI into meaningful business impact.Host Kris Peeters talks with Joris Renkens, founder of AI product studio Guatavita, about how organizations can build AI solutions that truly work in practice.They discuss:Why innovation starts with the right problem, not the latest techHow to validate adoption early, before writing complex modelsWhat makes a high-performing AI product teamHow to manage technical debt while moving fastWhy flexible strategy matters more than fixed roadmaps🎙 Listen & subscribe on Spotify🌐 More at www.dataminded.com

In this episode of The Data Playbook, we explore what it really takes to build high-performance data teams.Host Kris Peeters is joined by Rushil Daya, Senior Data Engineer at Dataminded, who shares practical lessons from years of leading successful data teams across industries.They discuss:The link between data success and business valueHow mentoring beats documentation when upskilling teamsWhy testing and CI/CD matter more than flashy toolsWhat makes stakeholder communication essentialWhy Agile is nothing without real feedback loops🎙 Watch on YouTube: https://youtu.be/JEPPVakHfhA🌐 More at www.dataminded.com

In this episode of the Data Playbook podcast, we explore what it really takes to build sustainable, data-centric organizations, moving beyond tooling and dashboards toward lasting value.Host Kris Peeters is joined by Jonny Daenen (Knowledge Lead at Dataminded), who shares insights from years of helping organizations evolve their data strategy across sectors. Together, they discuss why data platforms, domain-owned data products, and people-first operating models are the foundations of modern data success.🔍 Topics covered:Why dashboards aren’t enough for true data maturityFrom central chaos to federated teams and self-serviceThe role of governance in enabling deliveryHow AI and LLMs reshape data tooling, ownership & valueWhat “data-centric” really means , and why most fail to get there🎙 Hosted by Kris Peeters👥 With Jonny Daenen, Dataminded🌐 Visit our website for more.

In this episode of The Data Playbook, we take a technical look at SQLMesh, a data transformation framework designed to improve the workflow and reliability of SQL-based data pipelines. Hosted by Kris Peeters, the episode features Michiel De Muynck, Senior Data Engineer at Dataminded, who provides a deep dive into SQLMesh’s internal mechanics, including its use of semantic analysis and isolated runtime environments.Michiel outlines how SQLMesh differentiates itself from tools like dbt by incorporating a semantic parser for SQL, enabling structural validation and more precise error diagnostics during pipeline development. He also explains the implementation of virtual data environments, which allow data engineers to stage, test, and version transformations without impacting production datasets, supporting safer iteration and deployment processes.🎧 Listen to more episodes on Spotify: Data Playbook Podcast🌐 Visit our website for more: Website Link

In this special episode of "The Data Playbook" podcast, recorded live at the Data Mesh Live Event in Antwerp, Kris Peeters speaks with Data Mesh pioneers Jacek Majchrzak and Andrew Jones. They explore how Data Mesh addresses critical challenges in data management, including data bottlenecks, governance, and decentralization. With years of experience in the field, both Jacek and Andrew share practical lessons from their journeys and offer actionable insights into implementing Data Mesh effectively.The conversation covers:Solving data bottlenecks through decentralized architecturesImproving governance with federated modelsAligning data strategy with business goals for impactful resultsUnderstanding the importance of incremental implementationMoving beyond "data silos" towards a more flexible, scalable approachJacek and Andrew provide real-world examples of how Data Mesh can transform your data infrastructure, sharing lessons on what works, what doesn’t, and how to manage a successful Data Mesh implementation. If you're looking to overcome common data management challenges like governance and scalability, this episode is packed with practical advice.Books Referenced by Our Speakers:📚 Data Mesh in Action by Jacek Majchrzak - https://a.co/d/4i5HUcY📚 Driving Data Quality with Data Contracts by Andrew Jones - https://amzn.eu/d/aMQRFH1Stay tuned for more episodes on Data Mesh and other important topics in data architecture by following "The Data Playbook" on Spotify.🎧 Watch the full episode on Youtube⁠⁠🌐 Learn more on our website

Join us in this episode of The Data Playbook as we explore the sense and nonsense of data modeling with Jonas De Keuster, VP of Product at VaultSpeed. Jonas takes us through his journey in the world of data automation, discussing the role of data integration, data vaulting, and how modern data products are built using structured models. From dimensional modeling to the complexities of integrating data across multiple systems, Jonas shares practical insights into how organizations can scale their data operations.Topics covered include:Data Modeling Techniques: Data Vault vs. Dimensional ModelingData Automation and IntegrationBuilding Data Products with Scalable ModelsHow to Manage Data Changes and Evolving Business NeedsReal-world Challenges in Data PlatformsWhether you're leading a data team or just beginning your journey, this episode is a must-listen for anyone interested in the future of data architecture. Tune in for expert advice on building integrated data solutions that deliver real business value.To learn more, visit our website, or Watch more episodes on YouTube.

What do you do when GDPR forces your cloud project to stop—and years later, you need to go back? In this episode, Niels Melotte, Data Engineer at Dataminded, unpacks the journey of a government agency that migrated from the cloud to on-prem and then back to the cloud again.And here’s the kicker: the Big Bang migration only took 14 hours. No downtime. No data loss. No angry users.🔍 In this episode, we discuss:Schrems II and why it sent European governments off the cloudAWS Nitro Enclaves & external key management for GDPR complianceWhy the on-prem platform failed to meet uptime guaranteesWhat “purpose-based access control” means and why it mattersThe value of standardizing with dbt and StarburstHow data product thinking shaped the migration strategyLessons learned about trust, stakeholder communication, and platform maturityThis isn’t a fluffy case study. It’s a practical guide full of engineering tradeoffs, real-world headaches, and long-term lessons. A must-listen for data leaders, engineers, architects, and anyone dealing with sensitive data and complex infrastructure decisions.🎧 Want more episodes?Watch or Listen to all episodes of The Data Playbook on Spotify: 👉 https://open.spotify.com/show/78z3kdyBSKiURz1VnTVP9l?si=781abec722264306Show notes, episodes & resources:👉 https://www.dataminded.com/resources/podcast#CloudMigration #PublicSector #GDPR #DataGovernance #AWS #DataPlatform #dbt #Starburst #BigBangMigration #TheDataPlaybook #Dataminded

In this episode of The Data Playbook, we dive deep into a critical, often-overlooked question: What does it mean to build sustainable data products? And no, we’re not just talking ESG dashboards or carbon reporting.🎙️ Host Kris Peeters is joined by Geert Verstraeten, a seasoned data scientist, founder of Python Predictions, and now a Co-Lead at The Data Forest—a consultancy that puts purpose and sustainability at the core of every data project.Over a candid and rich conversation, Geert shares:How he transitioned from startup founder to corporate leader and back to startup life—with a sustainability mission.Why many data projects fail not because of tech, but because of missed alignment and poor adoption.What sustainable data products really are: tools that not only minimize environmental impact, but also stand the test of time—well-documented, actually used, and aligned with real business needs.Why selecting the right clients and projects is the first step toward impact, not just profit.How the Data Forest scores potential engagements using a unique framework: Head, Heart, and Hands.💡 Along the way, you’ll hear thought-provoking takes on:The role of documentation in sustainability.Why good data work isn’t about building everything real-time or at scale—especially when you’re not Google.The paradox of GenAI and compute-heavy models in a world striving for tech responsibility.Whether you're a data engineer, architect, scientist, or team lead, this episode challenges you to rethink what a "good" data project looks like.👀 If you’ve ever built something technically brilliant that no one used, this episode is for you.Hit play to hear:What drives Geert and his team at The Data ForestHow to make better decisions on project scoping and client fitWhy data professionals need to talk to stakeholders much earlier—and more often

What Not to Build with AI: Avoiding the New Technical Debt in Data-Driven OrganizationsIn this episode of The Data Playbook, we explore a crucial and often overlooked question in the age of generative AI: not what to build—but what not to build.Host Kris Peeters (CEO of Dataminded) is joined by seasoned data leaders Pascal Brokmeier (Head of Engineering at Every Cure) and Tim Schröder (AI & Data Transformation Lead in Biopharma), to talk about the dark side of unlimited AI capabilities: technical debt, fragmented systems, and innovation chaos.Topics we dive into:Why generative AI lowers the barrier to building—but increases long-term complexityThe risks of treating LLMs as “magical oracles” without governanceHow RAG systems became the default architecture—and why that’s dangerousThe zoo vs. factory dilemma: how to balance AI experimentation with structureMaster data vs. knowledge graphs vs. embeddings – when and why each breaks downWhat Klarna got right (and wrong) by replacing SaaS tools with AI-generated internal appsThe growing importance of AI literacy, data maps, and platform thinkingReal-world examples of AI agents autonomously debugging systems—and when that’s terrifyingWe ask tough questions like:Are enterprises just building themselves into a new kind of mess, faster than ever before?Is the AI hype driving us toward “build now, regret later”?Should you really let every department build their own AI stack?Whether you're a data engineer, data architect, AI product lead, or a data strategist, this episode is a must-listen. We’re cutting through the hype to figure out where the real value is—and where the future tech debt is quietly piling up.🧠 Key quote:"If you can't tell me why you're building it, maybe you shouldn't be building it at all."💡 Tune in to learn how to stay smart, intentional, and strategic when it comes to building with AI.#TheDataPlaybook #DataEngineering #AIinBusiness #TechnicalDebt #RAG #LLMs #DataStrategy #EnterpriseAI #DataGovernance #DataLeadership #KnowledgeGraphs #GenerativeAI #AIinHealthcare #AIProduct #Dataminded

What does it really take to build a modern data architecture from the ground up? In our very first episode of The Data Playbook, host Kris Peeters, founder and CEO of Dataminded, sits down with Thorsten Foltz, seasoned data architect and engineer, to unpack what works (and what doesn’t) when designing scalable, future-proof data platforms.With a focus on real-world tradeoffs, this episode explores:Cloud vs on-prem vs hybrid: how to choose the right infrastructureThe rise of Data Mesh and when it actually makes senseWhy fake news isn’t just a media problem—it’s a data problem inside companies tooVendor lock-in, cloud sovereignty, and the growing relevance of European alternativesThe balance between open source and managed services: cost, control, and complexityWhy team culture and communication often make or break your data strategyWhat engineers can really expect from LLMs in the data stack (spoiler: they're not replacing data modeling any time soon)Whether you're a data engineer, architect, analyst, or tech leader, this conversation goes far beyond buzzwords. You’ll hear practical lessons, hard-earned insights, and a few uncomfortable truths about how companies actually manage data today—and how they should rethink it for tomorrow.