Data Quality

Data quality is the degree to which data is accurate, complete, consistent, valid, unique, and timely enough to be trusted for its intended use. It is typically measured along these six dimensions, expressed as quantifiable rules — for example, 'national ID numbers must be 10 digits' or 'no order may reference a customer that does not exist' — and tracked over time through automated tests and scorecards.

Mature organizations treat data quality as an operational discipline rather than a one-off cleanup: rules run continuously against production data, failures raise alerts, and accountable owners (data stewards) remediate at the source instead of patching reports downstream.

For a Saudi Data Management Office, data quality is not optional. It is a dedicated domain within the National Data Management Office (NDMO) framework, which spans 15 domains, 77 controls, and 191 specifications, and it requires entities to define quality metrics, monitor them, and demonstrate remediation. Beyond compliance, reliable national indicators, digital government services, and AI initiatives all inherit the quality of their underlying data. A DMO that can show measured, improving quality scores per data domain has evidence for regulators and credibility with the business.