Ep. 102 - Data Governance: Angelika Rinck

To implement AI (and processes) correctly, you need good data. But what does that mean? Well, firstly it means that you can define your data product and to achieve that you need good data governance.

But are we now in a super-nerdy topic? No, this is what we all do in some form or another ... but in different fidelities and maturities.

To shed some light on the topic of data governance, we invited Angelika Rinck for this episode. She started her career studying public administration and then served in the German federal police before switching to the regular industry (in the aerospace industry, and while that might not be enough, she studied economics in parallel).

Somehow she found her way into consulting and is working now in digitalization and IT projects. Her main focus here is product lifecycle management and data governance.

In this episode of the podcast, we talk about:
Angelika’s career journey: from e-commerce working student in Hamburg to aerospace, engineering, and ultimately major IT and data governance initiatives.
Her first agile project—complete with a physical Kanban box—sparked her love for IT project management and structured delivery.
A detour into underwater orienteering reveals surprising parallels to data work: precision, navigation, and making decisions in the dark.
Defining data governance: the framework of rules, processes, and responsibilities that guide how organizations create, use, secure, and improve data.
Why it matters: Governance drives clarity, accountability, and value creation—not just control or compliance.
Understanding the difference between data governance (framework and value creation) and data management (the operational “doing”).
A common failure pattern: organizations naming “business data stewards” without training, tooling, or understanding the expectations.
Governance only works when decentralized experts feed real issues into a central team—not when policies are pushed top-down in isolation.
Data products demystified: they’re the outcome of well-governed data—reusable, high-value information assets that improve processes, decisions, speed, or cost.
Real examples: using historical field data instead of simulation data to accelerate engineering calculations or using decades of bird-flight video to predict weather with AI.
Risks of bad data with AI: incorrect system guidance, support tickets exploding, contradictions between outdated documents, and misplaced trust in “the easy button.”
Governance foundations: critical data identification, metadata transparency, ownership, RASCI clarification, and understanding who creates, changes, and consumes data.
The messy reality: access rights often don’t match process needs—leading to shortcuts, bypasses, and unintended process redesign opportunities.
Final takeaway: data governance isn’t bureaucracy—it's a structured path to value, clarity, and safer AI adoption, but it requires real effort, definitions, ownership, and cultural change.

You can reach Angelika on LinkedIn here:   / angelika-rinck-b93a7019b  .

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