Getting Ready Before You Build
Validate data at the point of entry (shift left), add automated observability for drift and anomalies, and define the measurable outcome and KPIs before building. Readiness is a discipline you start before the first pilot, not a cleanup after it fails.
Shift left
Catch data problems at ingestion, not downstream. Fixing an error where it enters is far cheaper than tracing it after it has polluted every system that consumed it.
Shifting validation "left", to the point of data entry, stops errors before they propagate. The alternative, cleaning data downstream after it surfaces as a problem, is the firefighting that consumes 30–40% of data-team time (industry research). Validate at ingestion and the whole downstream burden shrinks.
Observability and outcomes
Automated observability catches drift and freshness failures in real time; defining outcomes up front gives readiness a target. Together they turn data readiness from a vague aspiration into a measurable program.
Do not "get the data ready" in the abstract, get it ready for a specific, measurable outcome. Define the KPI the AI must move first; that tells you exactly which data has to be clean, connected, and governed, and keeps the effort from sprawling.
Frequently asked questions.
Should I fix all my data before starting AI?
No, that never finishes. Fix the specific data your first measurable use case needs, prove the value, then expand. Tie readiness work to a defined outcome so it has a clear scope and stopping point instead of sprawling indefinitely.