The #1 Mistake Companies Make With Data Governance

How data trust breaks—and how to rebuild it before AI makes it worse. In this episode of AI Radicals, host Satyen Sangani sits down with Nathalie Berdat, Data Director of Product at the BBC, to explore how one of the world's most trusted media institutions is rebuilding its data foundations for the AI era. Nathalie shares how she diagnosed a quiet trust crisis inside the BBC—teams producing conflicting numbers for the same metrics—and led a multi-year effort to fix it: identifying the handful of metrics that actually mattered, building certified "data products" as single sources of truth, and modernizing a legacy platform to support them at scale. She also unpacks why AI governance at a public institution carries different stakes than at a commercial company, how the BBC decides where genAI is (and isn't) allowed to touch editorial content, and what has to be true before agentic AI can responsibly run across an organization like the BBC. "The governance isn't a compliance checkbox, it's closer to editorial standards. It has to be defensible to a journalist." Listen to this episode to learn: Why low trust in data often shows up as two teams presenting two different numbers for the same metric and how to fix it Why the BBC treats AI governance as an editorial issue, especially when it comes to recommendations and content curation Why agentic AI requires clear data ownership, documented lineage, and machine-readable governance before it can be deployed responsibly