Mixed Model Arts
summary
Argues that application, analytics and ML modeling must converge because siloed models and messy upstream data break AI projects — industry failure rates hover around 80% and a RAND survey finds only 14% feel capable of deploying AI. Recommends concrete practices: relearn relational theory and conceptual/logical/physical layers, use DBT-style pipelines, semantic layers, knowledge graphs and embeddings, and shift left and right with producers and consumers. Flags a missing piece: automated conceptual→physical sync across systems; current tooling is insufficient.
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chapters (8)
- 0:00Mixed model arts intro
- 7:18Data modeling as art
- 13:23Criticisms of traditional modeling
- 21:53Modern data complexities
- 27:57Apps analytics AI convergence
- 30:46Mixed model arts knowledge
- 40:37Technology gaps in MMA
- 43:06Conclusion
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