DATA MODELLING IS DEAD! LONG LIVE DATA MODELLING!
summary
Claims that data modelling is both neglected and essential for AI success, arguing that ignoring conceptual and logical modeling leads to brittle systems; shows real failures like thousands of DBT models nobody understands, ad‑hoc schema-on-read/one‑big‑table anti‑patterns, and rising entropy that fuels AI hallucinations. Recommends resurrecting modeling patterns (Data Model Resource Book, Nimble Elephant), shifting left with data contracts, and treating modeling as an end‑to‑end, mixed‑model practice across the data life cycle.
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chapters (8)
- 0:00The decline of data modeling
- 2:14Data modeling in the AI era
- 3:47Defining data models
- 7:30Common modeling pitfalls
- 9:31Conceptual and physical levels
- 12:30Causes of the modeling crisis
- 18:03Modernizing data practices
- 26:06Conclusion and Q&A
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