Data degrades continuously
Duplicate customers accumulate, fields go unfilled, values drift from their permitted set, and relationships break. None of it announces itself.
Businesses discover their data quality problems during an audit, a migration or a reporting dispute — all of which are expensive moments to find out.
What to check automatically
- Duplicates by every plausible key, including fuzzy matching on names
- Missing values in fields that should always be populated
- Impossible values — negative quantities, future dates of birth, amounts outside plausible ranges
- Referential gaps — records pointing at things that no longer exist
- Format inconsistency — the same thing written several ways
Run them on a schedule
- Daily, on the records created or changed that day
- Weekly, across the whole dataset for slower-moving problems
- Report as a count and a list, not as an alert per record
- Track the trend, because a rising count means something changed
Fix causes, not instances
A recurring data problem is a process problem. Duplicate customers accumulate because the creation process does not check; missing fields accumulate because they are not required.
| Symptom | Usual cause |
|---|---|
| Duplicate customers | No check at creation |
| Missing fields | Not required, or required later |
| Inconsistent formats | Free text where a list belongs |
| Orphaned records | Deletion without cascade |
| Impossible values | No validation at entry |
It pays for itself before a migration
Any future system change — a migration, an integration, a new reporting layer — is made considerably harder by poor data quality. Fixing it continuously is far cheaper than fixing it under project pressure.
It also means your reporting is trustworthy in the meantime, which is worth something on its own.