The Data Playbook Podcast
The Data Playbook Podcast
Podcast Description
🎙️ The Data Playbook is a podcast where we aim to build a playbook for data leaders. We do that through a series of interviews with other data leaders, data practitioners and data experts. In each episode, we break down real-world data challenges: from building modern architectures and embracing Data Mesh to navigating cloud sovereignty, we help you make smarter decisions one play at a time.
Podcast Insights
Content Themes
The podcast centers around themes such as data architecture, AI governance, and technical debt, with episodes tackling specific issues like the pitfalls of generative AI, the principles of Data Mesh, and cloud sovereignty. For example, one episode explores the dangers of treating LLMs as tools without proper oversight, while another discusses how to construct a modern data architecture in the age of misinformation.
🎙️ The Data Playbook is a podcast where we aim to build a playbook for data leaders. We do that through a series of interviews with other data leaders, data practitioners and data experts. In each episode, we break down real-world data challenges: from building modern architectures and embracing Data Mesh to navigating cloud sovereignty, we help you make smarter decisions one play at a time.
What Not to Build with AI: Avoiding the New Technical Debt in Data-Driven Organizations
In this episode of The Data Playbook, we explore a crucial and often overlooked question in the age of generative AI: not what to build—but what not to build.
Host Kris Peeters (CEO of Dataminded) is joined by seasoned data leaders Pascal Brokmeier (Head of Engineering at Every Cure) and Tim Schröder (AI & Data Transformation Lead in Biopharma), to talk about the dark side of unlimited AI capabilities: technical debt, fragmented systems, and innovation chaos.
Topics we dive into:
Why generative AI lowers the barrier to building—but increases long-term complexity
The risks of treating LLMs as “magical oracles” without governance
How RAG systems became the default architecture—and why that’s dangerous
The zoo vs. factory dilemma: how to balance AI experimentation with structure
Master data vs. knowledge graphs vs. embeddings – when and why each breaks down
What Klarna got right (and wrong) by replacing SaaS tools with AI-generated internal apps
The growing importance of AI literacy, data maps, and platform thinking
Real-world examples of AI agents autonomously debugging systems—and when that’s terrifying
We ask tough questions like:
Are enterprises just building themselves into a new kind of mess, faster than ever before?
Is the AI hype driving us toward “build now, regret later”?
Should you really let every department build their own AI stack?
Whether you’re a data engineer, data architect, AI product lead, or a data strategist, this episode is a must-listen. We’re cutting through the hype to figure out where the real value is—and where the future tech debt is quietly piling up.
🧠 Key quote:”If you can’t tell me why you’re building it, maybe you shouldn’t be building it at all.”
💡 Tune in to learn how to stay smart, intentional, and strategic when it comes to building with AI.
#TheDataPlaybook #DataEngineering #AIinBusiness #TechnicalDebt #RAG #LLMs #DataStrategy #EnterpriseAI #DataGovernance #DataLeadership #KnowledgeGraphs #GenerativeAI #AIinHealthcare #AIProduct #Dataminded
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