Data Quality Management Guide
Define 'Good Data' Before You Profile or Buy a Tool
"Data should be mostly accurate" is not a threshold
Vague targets are unenforceable and useless for reporting, yet most programs start profiling records or shortlisting tools before anyone has agreed what "good" means for each dataset. Without a shared, measurable definition, remediation efforts lack direction and audit evidence lacks credibility.
This guide sets the definition first: the four dimensions that matter for regulated records, each tied to a numeric threshold and a specific regulatory minimum, before any profiling or tooling work begins.
The four dimensions, thresholded
A worked threshold structure ties each dimension to a field, a numeric target, and a breach action.
What you can do once you've read it
Agree one measurable standard of 'good'
Give governance, legal, and operations a shared numeric definition for each dataset before a program starts, so remediation has direction and audit evidence holds up.
Tie each control to the article it satisfies
Map thresholds and workflows directly to GDPR 5(1)(d), 5(2), 7, 16, and 22, plus CCPA, FCRA, and the EU AI Act, instead of defending controls in the abstract.
Lift concrete examples into your own program
Adapt the threshold tables, remediation SLA tiers, KPI sets, and alert levels rather than inventing your own from scratch.
Buy tooling against the right requirements
Evaluate profiling, rules-engine, and lineage capabilities against the controls your regulatory environment actually imposes, before you assess features or pricing.
Grounded in the current rulebook
Every control in the guide maps to the framework it satisfies.
Frequently asked questions
No. The numeric thresholds, SLA tiers, KPI sets, and alert settings are DataRidge-suggested examples to adapt to your own risk assessment, not regulatory requirements or industry standards. The regulatory obligations they map to are cited explicitly.
No. It is written for compliance and governance teams, with audit-trail requirements throughout, so privacy officers, legal teams, and DSAR and consent operators can use it alongside technical staff.
Defining and thresholding the dimensions comes first, then the guide follows the full lifecycle end to end: profiling and discovery, validation and rules, remediation workflows, monitoring and scorecards, AI readiness, and tooling selection.
No. It is provided for general informational and educational purposes only. References to GDPR, CCPA, FCRA, and other laws are summaries for context, and you should verify current requirements against primary sources and qualified counsel.