The Data Playbook Podcast

Andrew Jones, staff data engineer at LocalStack, returns to the Data Playbook podcast to make the case that giving business teams raw access to company data was never enough. The real fix is data contracts, tooling built around how producer teams already work, and platforms with guardrails baked in rather than bolted on through review.

Jones and host Kris Peeters, CEO of Dataminded, cover a data platform that failed because it forced producers into unfamiliar tooling, and the one that replaced it by meeting those same teams where they already worked. They also dig into where self-service stops, why so many data teams hold onto being the bottleneck, and what happens to data semantics once AI agents start asking the questions people used to ask. Jones tells the story of an internal chatbot that gave him a feature-usage number he knew, on instinct, had to be wrong.

🌐 More at www.dataminded.com

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

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/

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

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/

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.

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

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

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

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:

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👉 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