Data quality is not a universal score. Data is high quality when it is trustworthy enough for a defined use, at the required time, with known limitations. The same dataset may be suitable for trend analysis but unsafe for an automated decision.
Measure fitness for a specific purpose
Connect each quality rule to a consumer and consequence. A missing field, duplicate identity, stale status, or invalid unit matters differently depending on the decision it supports.
Find problems at their source
Profiling and cleansing can repair symptoms, but sustainable quality comes from ownership, validation at entry, explicit contracts between systems, controlled reference data, and observability across pipelines.
Operate quality continuously
Track dimensions such as accuracy, completeness, consistency, timeliness, validity, and uniqueness. Use thresholds, alerts, issue ownership, root-cause analysis, and trend reporting instead of one-time clean-up projects.
Strong technology begins with the complete operating context. Connect the disciplines early, validate against real constraints, and design for the lifecycle—not merely the launch.