TD
The Data Playbook Podcast
Dataminded
🎙️ 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.
- 22 episodes
- fortnightly
- Avg 54 min
- English
Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
- TDS3 · E10September 2 · 1 hr 2 min
Data Contracts and Data Products: What AI Agents Need to Access Enterprise Data - The Data Playbook podcast with Simon Harrer & Kris Peeters
Simon Harrer, CEO and co-founder of Entropy Data, returns to the Data Playbook podcast to make a case every data leader will need to reckon with soon: AI agents need access to company data, and the access controls built for humans do not hold once an agent can request, use, and reuse that access without anyone reviewing it. Simon Harrer and host Kris Peeters, CEO of Dataminded, unpack the difference between data contracts and data products, how purpose-based access control catches an agent that drifts from the purpose it was granted, and what a semantic layer does for a company's data model, connecting an order ID in one system to the same field under a different name in another. They also get into data lineage, the return of data mesh now that agents need a foundation to work from, and the data product builder Simon Harrer's team runs on Claude Code. The conversation draws on the BARC research spotlight Entropy Data sponsored, "A Data Marketplace Is What Your Agents Need," written by BARC analyst Florian Bigelmaier. 🌐 More at www.dataminded.com
- TDS3 · E9August 20 · 56 min
Data Mesh After the Hype: Why Data Products Matter More Than Ever
Many organisations embraced Data Mesh. Many also discovered that organisational change is much harder than technology change. In this episode, Kris Peeters speaks with Arif Wider about what remained valuable after the initial hype around Data Mesh settled. They discuss why Data Products have become the practical foundation for modern data organisations, where Data Contracts fit into the picture, and how organisations can modernise legacy platforms without starting over. The conversation also looks ahead at AI-assisted software engineering, cross-company data sharing, and the skills future data teams will need. Topics include: Data Mesh in practice Data Products Data Contracts Data Architecture Legacy data warehouses AI agents in software engineering The future of data engineering 🌐 More at https://www.dataminded.com/ Connect with Arif Wider Website: http://arifwider.com ResearchGate: https://www.researchgate.net/profile/Arif-Wider Book - Data Mesh in Action: https://www.oreilly.com/library/view/data-mesh-in/9781098108502/
- TDS3 · E8August 5 · 42 min
Why AI Agents Need Knowledge Graphs, Not Just Data - The Data Playbook Podcast with Kris Peeters & Eric Broda
Very few companies manage 10,000 of anything today, and Eric Broda thinks that's the real challenge behind AI agents at scale. Broda runs Broda Group Software, helping banks and insurers build agent ecosystems he calls the agentic mesh. With host Kris Peeters, CEO of Dataminded, Broda explains why agents need a data mesh foundation, why one client tracks R&D tax credit exposure by watching code commits in real time, and why his team moved from fine-grained knowledge graphs to coarse markdown files after RAG broke down at scale. Find Eric Broda's work at agenticmesh.substack.com and on LinkedIn and YouTube under The Agentic Mesh Podcast. 🌐 More at www.dataminded.com
- TDS3 · E7July 22 · 36 min
How Tomorrowland Keeps Data Simple While Scaling Globally
Modern data platforms are becoming more powerful. They are also becoming more complex. In this episode of The Data Playbook, Kris Peeters talks with Wannes Rosiers, Head of IT & Development at WEAREONE.world, the company behind Tomorrowland, about why many organisations solve familiar data problems with increasingly complicated technology. They explore what actually matters when building data platforms: delivering quickly, keeping systems maintainable, bringing business knowledge closer to engineering teams, and preparing for AI without rebuilding the entire technology stack. The conversation covers: Data platform architecture Data Mesh Data engineering AI adoption Platform simplicity Data leadership Enterprise architecture 🌐 More at https://www.dataminded.com/
- TDS3 · E6July 8 · 54 min
How NMBS/SNCB Uses Data Science to Improve Public Transport
Building advanced analytics is rarely a technology challenge alone. Success depends on understanding the organisation, delivering value incrementally and enabling teams to solve real operational problems. In this episode, Kris Peeters speaks with Max Hubeau, Manager of the Advanced Analytics Team at NMBS/SNCB. They discuss how a central analytics team works alongside business teams, how data science supports train punctuality, passenger counting and energy forecasting, and why AI becomes valuable when it fits existing operations instead of disrupting them. The conversation also explores data platforms, AI-assisted development, and practical lessons for CIOs, CDOs, Heads of Data and analytics leaders building data capabilities inside large organisations. 🌐 More at https://www.dataminded.com/ Follow Dataminded for more conversations with data leaders shaping the future of Data & AI.
- TDS3 · E5June 24 · 58 min
Agentic AI in Production: What Data Leaders Need to Know Before Scaling AI
Agentic AI is everywhere. Production-ready AI is not. In this episode, Kris Peeters talks with Dataminded Data Engineer Jesus Garcia about the gap between AI hype and enterprise reality. Drawing on lessons from building a large-scale Agentic AI platform, they discuss security, governance, user adoption, change management, AI skills, and the future role of engineers in an AI-first world. For data leaders, CIOs, CDOs and technology executives, this conversation provides practical insights into what it takes to deploy AI systems that people trust and actually use. 🌐 More at https://www.dataminded.com/ Topics covered: Agentic AI, Enterprise AI, RAG, AI Security, AI Governance, Change Management, Data Leadership, AI Adoption, Data Engineering. Chapters: 00:00 Agentic AI in Production: Introduction 01:34 Enterprise Knowledge Management with AI Agents 05:19 RAG, Vector Search and Agentic AI Explained 07:36 Deploying Agentic AI at Enterprise Scale 12:14 AI Governance, Security and Access Control 17:06 What Data Leaders Can Learn from AI Security Incidents 25:34 Will AI Replace Software Engineers? 40:20 Why Coding Is Easier for AI Than Business Decisions 43:36 Real Productivity Gains from AI Skills and Automation 49:35 The Limits of Large Language Models 56:44 Change Management: The Real Challenge of AI Adoption
- TDS3 · E4June 11 · 1 hr 2 min
Can We Outsource Thinking? AI, Education, and the Future of Knowledge Work
Agentic AI is changing how we build software, manage data, conduct research, and learn new skills. But as AI takes over more cognitive tasks, a fundamental question emerges: what capabilities do humans still need to develop themselves? In this episode, Kris Peeters sits down with Frank Neven, Professor of Computer Science at Hasselt University and Vice Director of the Data Science Institute, to discuss the future of data engineering, AI-assisted learning, database systems, and human-AI collaboration. The conversation explores: Why understanding remains essential in an AI-driven world How universities are adapting to AI-powered education What the latest database research tells us about the future of data platforms The rise of agentic coding and AI-native software development How AI is transforming scientific research Why structured knowledge and semantic data are becoming more valuable This episode is particularly relevant for data leaders, CIOs, CDOs, architects, and engineering teams navigating the rapid evolution of AI. 🌐 More at https://www.dataminded.com/ #DataEngineering #AgenticAI #DataLeadership #ArtificialIntelligence Chapters: 00:00 From Theory to Data Engineering 03:20 AI and Healthcare Data Integration 06:00 The Data Science Institute in Practice 19:00 Why Computer Science Education Matters 25:40 AI in Education: Opportunity and Risk 35:00 You Can Outsource Reasoning, Not Understanding 38:45 Agentic AI and the Future of Databases 45:50 How AI Is Changing Research 51:45 Personal AI Systems and Knowledge Management 59:30 Staying Open-Minded in Technology
- TDS3 · E3April 9 · 52 min
The Data Challenge behind the Einstein Telescope - The Data Playbook Podcast with Kris Peeters & Tjonnie Li
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 matter how black hole collisions are measured why the Einstein Telescope could transform European science the data, compute, and storage challenge behind next-gen physics what academia can learn from industry about automation and orchestration how 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 #DataPlaybook Chapters: 00:00 Intro: Tjonnie Li joins The Data Playbook 02:08 What gravitational waves are - in plain English 05:19 Why science is becoming data-driven 11:28 How we measure black hole collisions today 15:50 The Einstein Telescope: ambition, timeline, and European bid 19:44 The data infrastructure challenge: from terabytes to petabytes 30:56 AI, automation, and the idea of a “science factory” 38:50 Why this matters for Europe, innovation, and society
- TDS3 · E2March 26 · 1 hr 1 min
Scaling Data in Aviation: Inside Brussels Airlines’ Data Strategy - The Data Playbook Podcast with Kris Peeters & Tom Holsteens
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 bottleneck What followed was a multi-year transformation focused on: Building a modern cloud data platform Moving to a data product architecture Enabling self-service analytics across teams Balancing central governance with decentral ownership Leveraging 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 teams The real difference between controlling vs. BI How to reduce bottlenecks with hub-and-spoke models A real use case: cutting food waste by 30% with data Why perfect data quality is a myth This 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.com Struggling with data bottlenecks, unclear ownership, or slow delivery? 👉 Explore our Data Product Workshop: https://www.dataminded.com/what-we-do/data-product-workshop Turn 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 context 02:30 What is controlling vs. business intelligence? 06:00 The problem: “spaghetti” data warehouse & bottlenecks 12:30 The transformation: platform, operating model & group strategy 19:00 Hub-and-spoke model & self-service analytics 27:30 Data products & the “restaurant” analogy 35:30 AI, data analysts & scaling data adoption 43:30 Real impact: reducing waste & driving business value
- TDS3 · E1March 16 · 54 min
Machine Learning in Energy: Forecasting, MLOps, and Business Impact - The Data Playbook Podcast with Kris Peeters & Jean-Michel Begon
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 learning ML team structure forecasting model development data pipelines and platform support model monitoring and performance review balancing business value with technical rigor Explore the full podcast series: The Data Playbook PlaylistDiscover more podcasts, blogs, and webinars: Dataminded ResourcesVisit the Dataminded website: https://www.dataminded.com/
- TDS2 · E11January 29 · 1 hr 5 min
How Dataminded Was Built: Kris Peeters on 11 Years of Data Engineering & Culture - The Data Playbook podcast with Kris Peeters & Pascal Brokmeier
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-us Or email: careers@dataminded.com Chapters: 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
- TDS2 · E10January 14 · 1 hr 13 min
How OBI Built a Lean, High-Impact AI Function That Scales - The Data Playbook Podcast with Kris Peeters & Dr. Ruth Janning
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 direction How to choose between ML, GenAI and agentic AI Real-world retail AI use cases (recommendations, assortment, automation) How a 10-person team delivers outsized business value AI 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
- TDS2 · E9Dec 18, 2025 · 59 min
S2 E9. Data Science vs Data Engineering: Breaking the Wall - The Data Playbook Podcast with Kris Peeters & Jelena Grujic
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
- TDS2 · E8Dec 11, 2025 · 46 min
S2 E8. A Structured Framework for Building Successful Data Solutions - The Data Playbook Podcast with Kris Peeters & Frederic Vanderveken
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 start How to anchor your work in business strategy, not technology How to run problem discovery interviews that surface real headaches, not minor annoyances Prioritising solutions using five lenses: customer, growth, money, pragmatic feasibility and differentiators Building a quantifiable business case and defining success upfront If 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
- TDS2 · E7Nov 27, 2025 · 32 min
S2 E7. Data Engineering Meets Excel: Building Explainable and Reliable Decision Models with River Solutions
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 Excel Silent errors, risk, and the real cost of debugging formulas Visual flow-based modelling and model maps inside Excel Built-in checks for missing data, duplicates and broken lookups How AI copilots helped build River, and why AI won’t replace transparent models The 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
- TDS2 · E6Nov 20, 2025 · 58 min
S2 E6. 5 Years Kate 🎂: Inside KBC’s AI Playbook - The Data Playbook Podcast with Kris Peeters & Dr. Barak Chizi
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 production Developed an AI-driven anti-money laundering platform and commercialised it for other banks Scaled Kate, now celebrating 5 years and upgraded with GPT. Uses the U-model to govern AI safely from idea to production Keeps ROI at the centre of every AI project Stays vendor-independent while still leveraging hyperscaler LLMs Builds diverse, high-calibre AI teams with a rigorous recruitment approach Explores soft logic and modelling customer intent as the next frontier of financial AI If 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/10.1007/978-3-031-58233-2 Dan Ariely – Predictably Irrational👉 https://www.amazon.com/Predictably-Irrational-Revised-Expanded-Decisions/dp/0061353248/ ⏱️ Chapters 00:00 – 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
- TDS2 · E5Nov 13, 2025 · 55 min
S2 E5. Beyond Hyperscalers: How to Run Modern Data Platforms on European Clouds - The Data Playbook Podcast with Kris Peeters & Niels Claeys
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 apps Table formats 101: why Apache Iceberg over plain Parquet/CSV Query layer choices: Trino for open SQL across object storage & DBs Orchestration in practice: Airflow patterns, dependencies, SLAs Security & governance: OPA for fine-grained policies, IAM, catalogs Cost & ops: egress, managed services gaps, version lag, troubleshooting Team skills: what to hire for, and the “hard questions” Niels asks in interviews 🌐 More at www.dataminded.com — and subscribe! Chapters 00:00 Intro & why EU clouds now 04:40 Compliance & legal: GDPR, Cloud Act, sovereignty 11:55 Platform blueprint: Kubernetes + Iceberg + Trino + Airflow 20:30 Catalogs, OPA, IAM & access control 27:10 EU providers deep dive: Scaleway, OVH, Exoscale, UpCloud 36:20 Cost, encryption/KMS, egress & performance 43:10 Best-of-breed vs all-in-one (and glue work) 51:00 Getting started: IaC, Argo CD, day-2 ops 56:40 Hiring: interview signals & practical takeaways Keywords EU 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
- TDS2 · E4Nov 5, 2025 · 57 min
S2 E4. Build vs Buy in the GenAI Era: Inside Belfius’ Data & AI Strategy - The Data Playbook Podcast with Kris Peeters & Hannes Heylen
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 learn How to pick first AI cases that prove €ROI (fraud models) Designing a claims copilot: summarization, completeness & coverage checks Where AI agents fit (GenAI + ML + humans) across the claims flow Build vs. buy in 2025: foundation models, vendor flexibility, cost control Content/data governance as the make-or-break for LLM apps “First make it work, then right, then cheap”: an AI operating model for CIO/CDO Guest: Hannes Heylen, Head of Data & AI, Belfius Insurance 🌐 More at www.dataminded.com Chapters: 00:00 Why AI now in financial services 06:30 GenAI’s impact on text-heavy insurance processes 18:40 AI agents across claims 31:00 Governance > model tweaks 38:00 Fraud detection: the € case 41:30 Claims copilot (“Nestor”) & lab-to-prod 55:00 Lessons for CIOs/CDOs Topics: ROI-first use cases • Claims automation • AI agents (GenAI + ML + human-in-the-loop) • Governance • Vendor flexibility & costs
- TDS2 · E3Oct 31, 2025 · 54 min
S2 E3. How imec scales research with data platforms: governance, workbenches, and adoption - The Data Playbook Podcast with Kris Peeters & Wim Vancuyck
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 assets Four self-service workbenches for Power BI, Data Engineering, Data Science & AI, and Application Development A clear platform vision built on efficiency, scalability, and reliability A governance model that supports both compliance and creativity A pragmatic stance on shadow IT: embrace, standardise, and professionalize it A bottom-up adoption strategy driven by early adopters and community engagement 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. 🎙️ 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