Data Quality Fundamentals

As of 2026-05-29

Data quality issues are the leading cause of business distrust in analytics systems. The consultant who quantifies DQ — with dimension-level scores, profiling evidence, and business-agreed thresholds — converts a vague complaint into a governance artefact and an advisory engagement. The DQ scorecard is the anchor. The six dimensions (completeness, accuracy, consistency, timeliness, uniqueness, validity) each require a different measurement technique; conflating them leads to remediation that never finishes. Post-go-live monitoring via Datasphere Data Quality Rules is the only mechanism that prevents DQ regression after migration.