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: [email protected]

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

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

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 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

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

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:

• 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

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

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

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 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