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:
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
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:
🎧 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 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
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:
🌐 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
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
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
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:
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
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:
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 governance
07:45 Federation vs. centralization (and pitfalls)
13:10 From 300 owners to 15 domains
19:40 Designing domains that don’t break with reorgs
26:05 Data products vs. datasets—what changed
33:20 Governance by design: policy → product
40:05 Measuring maturity & building momentum
47:30 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 companies
How data products and contracts drive interoperability
Why AI and MCPs will redefine how data is shared and governed
The future of Data Mesh and the rise of data marketplaces
A conversation packed with real-world lessons for Data Leaders, CIOs, and CDOs driving digital transformation.
🌐 More at www.dataminded.com
🔗 Resources from the episode:
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:
This 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:
If 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:
🎙 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:
🎙 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:
🎙 Hosted by Kris Peeters
👥 With Jonny Daenen, Dataminded
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:
Jacek 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.
🎧 Watch the full episode on Youtube
Stay tuned for more episodes on Data Mesh and other important topics in data architecture by following "The Data Playbook" on Spotify.
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:
Whether 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 cloud
AWS Nitro Enclaves & external key management for GDPR compliance
Why the on-prem platform failed to meet uptime guarantees
What “purpose-based access control” means and why it matters
The value of standardizing with dbt and Starburst
How data product thinking shaped the migration strategy
Lessons learned about trust, stakeholder communication, and platform maturity
This 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=781abec722264306
Show 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:
💡 Along the way, you’ll hear thought-provoking takes on:
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 Not to Build with AI: Avoiding the New Technical Debt in Data-Driven Organizations
In 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 complexity
The risks of treating LLMs as “magical oracles” without governance
How RAG systems became the default architecture—and why that’s dangerous
The zoo vs. factory dilemma: how to balance AI experimentation with structure
Master data vs. knowledge graphs vs. embeddings – when and why each breaks down
What Klarna got right (and wrong) by replacing SaaS tools with AI-generated internal apps
The growing importance of AI literacy, data maps, and platform thinking
Real-world examples of AI agents autonomously debugging systems—and when that’s terrifying
We 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