Freshness register
Give changing data a review signal and an owner.
| Field | What makes it stale | Review signal | Owner |
|---|---|---|---|
| Contact details | A person changes role or company | Bounce, reply, or elapsed review date | Record owner |
| Account stage | Work moves without an update | No stage change after the agreed working period | Pipeline owner |
| Consent or contact state | A preference or policy changes | Source update or recorded request | Responsible commercial owner |
How good data goes wrong
Data decays in different ways depending on what it describes. Each type has a different fix.
Contact decay
People change roles, companies and contact details.
Job changes are the primary driver of contact decay. A contact who is accurately recorded as a VP of Operations at one company may leave for a new role, retire, or shift into a different function. Their email address becomes invalid. Their title is wrong. The company association no longer applies.
The rate of change depends on the audience, role, and workflow. No amount of careful data entry at intake prevents this type of decay; the useful response is to refresh or review the fields when a meaningful change is likely.
Company decay
Companies are acquired, restructured, renamed and closed.
Company data decays through acquisitions, rebrands, restructures, and closures. A company recorded accurately under one name is acquired and operates under a parent brand. A subsidiary becomes an independent entity. A division is disbanded.
Company decay is slower than contact decay but has broader consequences, when a company record is wrong, every contact associated with it inherits the error. One inaccurate company record can corrupt dozens of contact records silently.
Relationship decay
The recorded relationship no longer reflects reality.
A contact is recorded as the decision-maker for a purchasing relationship. They are promoted and a different person now owns the relationship. The record still shows the original contact as the primary. Follow-up and proposals go to someone who is no longer in the relevant role.
Relationship decay is often invisible in a CRM because the record remains technically accurate, the person exists, the company exists, the contact information works, but the commercial relationship it was created to represent has changed.
Field drift
Categorical fields drift as their meanings change.
A field that was defined clearly at launch accumulates ambiguity over time. New team members use values differently. A status that was used in one workflow gets repurposed for another. A category that described one type of customer is stretched to cover new customer types that do not quite fit.
Field drift is a form of semantic decay: the field is still populated, but what its values mean has changed, often gradually and without anyone making an explicit decision. Reports built on the field become unreliable not because the values are wrong but because the meaning is no longer consistent.
Why decay is structural
Decay is not prevented by better intake. It is managed by ongoing refresh.
A record entered perfectly twelve months ago can be wrong today through no fault of intake, the person left, the company rebranded. Decay is not fixed by tighter intake; it is managed by ongoing refresh.
