Streamlining Your Approach to Data
summary
Argues that GenAI success depends on governance, not just scale of data, and cites a Gartner stat that 60% of AI initiatives fail to reach production by end of 2026. Recommends concrete patterns: data contracts (YAML-style SLAs), data observability for freshness/quality alerts, versioned data products with lineage, a semantic layer and a data marketplace. Uses the Coinbase data breach and Apple/Goldman bias case as practical warnings that weak governance produces outages, ransom risk, and biased models.
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chapters (7)
- 0:00Introduction and background
- 3:52AI readiness and data literacy
- 10:08Data governance paradox
- 14:17Governance by design and data products
- 23:09Tools for AI-ready data
- 36:58Checklist for AI governance
- 40:26Key takeaways and conclusion
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