Business data quality review
When does data maintenance require dedicated review capacity? Census Bureau quality standards distinguish dimensions of information quality, while NIST and FTC guidance address responsible handling of information. For a small business, the operational evidence is found in corrections, unresolved exceptions, and the decisions delayed by unreliable records.
Accuracy is not the only dimension
A correct record can still be unusable if it is late, duplicated, missing an owner, or stored where the next worker cannot retrieve it. Data quality work therefore includes validation rules, exception routing, controlled correction, and a record of who approved the change.
Measures that expose review demand
Track first-pass acceptance, correction rate, duplicate rate, missing required fields, exception age, and reviewer minutes. Sample completed records rather than assuming every accepted entry is correct. Separate source-data defects from entry mistakes so the remedy matches the cause.
A bounded support role
An assistant can apply documented validation rules, flag exceptions, correct approved fields, and maintain a change log. Data owners retain authority over definitions, deletion, merging, sensitive access, and ambiguous records.
Conclusion
Correction rate and exception age reveal the review work hidden behind data-entry volume. Those measures help define a support role without giving an assistant authority over uncertain records.