Most organizations have data governance policies. Far fewer have data governance that actually works when it meets day-to-day operations. In this episode, Kenza and Pascal move beyond the theory and into the uncomfortable reality: why governance frameworks fail, why data quality is a leadership responsibility that nobody owns, and why the difference between having data and trusting data is exactly where most AI projects quietly fall apart.

The episode closes with a line worth writing down: a dashboard can be technically correct and still be organizationally untrustworthy. And another: trust should be earned by the system, not requested by the project team.

Key Takeaways

• Data governance only works when it protects value and speeds decisions — not when it is purely a compliance exercise people route around.

• The business owns the meaning of data. Technology owns the enablement. Governance connects the two.

• AI did not repeal garbage-in, garbage-out. It just made the garbage more eloquent. LLMs can mask data quality problems — which makes them more dangerous, not less.

• Trust in data must be built progressively: low-stakes decisions first, visible provenance, measurable error rates, and explicit human override.

• The opposite risk is equally dangerous: executives who trust the data too much. Confidence is not evidence. Mature organizations institutionalize questioning.

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