Data Quality

What is Data Quality?

Data quality isn't one number; it's a profile across six dimensions: accuracy (matches reality), completeness (fields are populated), consistency (same value across systems), freshness (recently updated), validity (passes format and rule checks), and uniqueness (no duplicates). Each dimension is measurable and improvable. The hardest part of running a data-quality program is not the tooling — it's the governance: who owns each dimension, what threshold counts as acceptable, what gets escalated when a metric falls below threshold. The teams that win at data quality treat it like a product, with metrics, SLAs, and a roadmap — not as an occasional cleanup project.

Why it matters

• Sets the ceiling on what any downstream automation can achieve.
• Builds rep and analyst trust in the system — and trust is the rate-limit on adoption.
• Compounds — every percentage point of quality improvement lifts every downstream metric.

Use cases

• Quality scorecard. Six-dimension report published weekly with trends.
• SLA per dimension. Explicit thresholds (e.g., email completeness > 95%) with owners.
• Improvement roadmap. Quarterly plan that targets the weakest dimension first.