Why the lakehouse work has to happen before the AI work
Most stalled AI programmes are not model problems. They are data contract problems that surfaced eighteen months late.
Enterprises rarely stall on AI because the model underperforms. They stall because nobody can say authoritatively what a customer is, which system owns that definition, or whether last quarter's numbers would reproduce today.
Contracts, then pipelines
A data contract is a schema plus an owner plus a promise about freshness and semantics. Without one, every downstream consumer re-implements its own interpretation, and the divergence only becomes visible when two dashboards disagree in front of an executive.
- Version schemas explicitly and treat a breaking change as an API break, with a deprecation window.
- Publish freshness and completeness as first-class metrics, monitored like uptime.
- Name a human owner per domain. Shared ownership of a table is no ownership.
Reproducibility is the real deliverable
Time-travel on your tables is worth more than another dashboard. Being able to re-run a report as of a date — and get the same answer — is what makes a data platform trustworthy enough to build automated decisions on.
You cannot automate a decision that the business does not yet trust a human to make from the same data.
The sequencing is unglamorous and it is not optional. Governed, reproducible foundations turn an AI programme from a series of pilots into something that compounds.
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