Data Quality

Data quality measures how fit data is for its intended use — whether it’s accurate, complete, consistent, and timely enough for the systems and people relying on it.

Dimensions commonly checked:

• Completeness — are required fields populated (no unexpected nulls)?
• Accuracy — does the data reflect reality (correct values, valid formats)?
• Consistency — does the same entity agree across systems (no conflicting records)?
• Timeliness — is the data fresh enough for the decision it’s feeding?

In pipelines, these checks are often automated as tests that run after each load and block downstream consumption if they fail.

See also: Data Governance