“Having data” is not “being able to decide”
Enterprises rarely lack reports. They lack consistent, traceable, explainable data products at the decision point. Conflicting metric definitions, master-data clashes, and delayed events can be patched by human experience in manual analysis — and become systemic errors under automation and generative workflows.
Decision-grade means clear definitions, clear ownership, latency matched to the business beat, and monitored quality. Below that bar, stronger models only translate noise more fluently.
Governance is not a document warehouse
Effective governance centers on who uses which data to decide in which scenarios: catalogs and lineage for discovery, quality rules for runtime, access and masking for compliance — not unread policy PDFs.
For AI, feature and knowledge sources for training and inference must sit in the same change-management system. When model versions and data versions diverge, incident reviews become finger-pointing.
Build the smallest trustworthy loop first
You do not need a perfect platform on day one. Select a high-value decision chain, close its minimal trustworthy data loop, then expand domains. Accept each step in business language — replenishment, underwriting, ticket routing — not merely “the pipeline ran.”
Data science and consulting should co-define the loop: business question, entities and events required, acceptable latency and error, and where models or rules sit. Reverse the order and technical debt accumulates under the banner of “AI projects.”