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/