The Data Playbook Podcast

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
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#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.
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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

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/