Make readiness a PDCA loop, not a one-time cleanup
Use PDCA to build a repeatable readiness loop for monitoring, checking, and improving AI-critical data.
The move: design the loop that keeps the data ready after launch. PDCA is a cycle: Plan the change, Do the change at controlled scale, Check the result against evidence, and Act by standardizing, adjusting, or escalating. For AI data readiness, the cycle matters because the target is not a one-time clean table. The target is a data asset that remains fit for AI use as the business changes. A useful PDCA loop is small and observable. Pick one critical risk: stale policy documents, missing owner IDs, unreviewed labels, duplicate customers, or schema drift. Define the threshold and the cadence.…
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