Data Quality Governance and Policy Integration
Data Quality efforts should be supported by and should support data governance policies. For example, governance policies can authorize periodic quality audits and mandate compliance to standards and best practices. All Data Management Knowledge Areas require some level of policy, but data quality policies are particularly important as they often touch on regulatory requirements. Each policy should include:
- Purpose, scope and applicability of the policy
- Definitions of terms
- Responsibilities of the Data Quality program
- Responsibilities of other stakeholders
- Reporting
- Implementation of the policy, including links to risk, preventative measures, compliance, data protection, and data security
Data Quality – Metrics in Data Quality Policy and Metrics
Much of the work of a Data Quality team will focus on measuring and reporting on quality. High-level categories of data quality metrics include:
Return on Investment for Data Quality
Statements on cost of improvement efforts vs. the benefits of improved data quality
Levels of Data Accuracy and Consistency
Measurements of the number and percentage of errors or requirement violations within a data set or across data sets
Data Quality Trends and Improvements
Quality improvement over time (i.e., a trend) against thresholds and targets, or quality incidents per period
Data Issue Management Metrics
- Counts of issues by dimensions of data quality
- Issues per business function and their statuses (resolved, outstanding, escalated)
- Issue by priority and severity
- Time to resolve issues
Conformance to Service Levels
Organizational units involved and responsible staff, project interventions for data quality assessments, overall process conformance
Data Quality Plan Rollout in Data Quality Policy and Metrics
- Data Quality Plan Rollout: As-is and roadmap for expansion
