What Dirty Data Costs

Dirty data does not announce itself. It does not throw an error. It quietly corrupts every report, every forecast, and every decision that depends on it. A duplicate contact inflates your pipeline by the value of every deal attached to it. A missing close date makes velocity analysis impossible for that deal. A wrong stage assignment distorts conversion rates for every stage the deal passes through. The cost of dirty data is not the time spent cleaning it — it is the sum of every bad decision made because someone trusted a number that was wrong.

1. Duplicate Records The most common and most expensive hygiene problem. When the same company exists as "Acme Corp," "ACME Corporation," and "Acme" in your CRM, every metric is inflated. Pipeline value, deal count, activity volume — all overstated. Deduplication is not a one-time project. It is a recurring process with automated matching rules.
2. Missing Required Fields Every deal without a close date, amount, or stage assignment is a hole in your data. Enough holes and the aggregate numbers become meaningless. Required fields should be enforced at the CRM level — not as a policy people forget, but as a validation rule that prevents saving an incomplete record.
3. Stale Records A deal last updated 90 days ago is not a deal. It is a ghost. Stale records inflate pipeline value, distort stage distributions, and create the illusion of activity where none exists. Automated staleness detection — flagging deals with no activity in 30+ days — is a minimum hygiene standard.