The Data Playbook Podcast

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

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/

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