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Data Quality Framework Explained
Understand data quality dimensions, monitoring, issue management, and how to build a framework that actually works.
What a data quality framework should cover
- Dimensions: accuracy, completeness, consistency, timeliness, validity
- Rules and thresholds
- Monitoring and alerting
- Ownership and escalation
- Root cause analysis and remediation
Make it operational
Assign ownership, track issues to closure, and make sure the framework is tied to a real business process.
Need a working framework?
We can help you design the rules, metrics, and stewardship workflow.
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