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Data-quality error taxonomy for outsourced review
A repeatable classification for missing, stale, duplicate, inconsistent, and unsupported records.
Method: classify defects by impact, detectability, reversibility, and likely process cause.
Key finding: a useful taxonomy guides correction and prevention; a raw error count does neither.
Review samples, preserve correction evidence, and route systemic errors to the process owner.
Sources: NIST Data Integrity; CISA Cybersecurity Basics; FTC Protecting Personal Information. Retrieved 2026-08-08.