Data Quality

Data Quality Assurance

Data quality assurance is the ongoing process of detecting errors, validating critical fields, and improving accuracy over time.

Allocator relevance: QA is how you avoid “wrong person” outreach and mandate mistakes that break trust with allocators.

Expanded Definition

QA includes automated checks (format, domain validity, duplication detection) and evidence-based review for high-impact fields (decision authority, roles, contacts). It also includes user feedback loops: correction requests are treated as signals to investigate and adjust evidence weighting.

Decision Authority & Governance

Governance defines QA thresholds, sampling rules, and escalation paths for disputed fields. QA must be auditable (what was checked, when, and what changed).

Common Misconceptions

  • QA is a one-time cleanup.
  • QA means “perfect data” (it means controlled error rates).
  • QA can ignore freshness (stale data becomes wrong).

Key Takeaways

  • QA is continuous and field-prioritized.
  • Focus QA on actionability fields first.
  • Tie QA to audit trail and correction workflows.
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