Data cleanup is often framed as a cost centre. Framed correctly, it is product work: every cleaned definition and validated field makes dashboards trustworthy and machine learning viable.

Start from the decision

Do not scrub everything. Identify the decisions leaders make weekly, then back into the fields and rules those decisions require. Cleanup scoped to decisions compounds; cleanup scoped to “all data” never ends.

Document ownership

Every critical field needs an owner and a quality rule. Without ownership, sanitization decays the week after the project ends. We treat documentation as part of the deliverable—not an afterthought.

Bridge to ML and analytics

Models amplify whatever you feed them. A modest, clean dataset beats a large, contradictory one. The same foundation powers executive dashboards and feature pipelines—so you pay for quality once.

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