Data Quality – Policy
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
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: Statements on cost of improvement efforts vs. the benefits of improved data quality
- Levels of Quality: Measurements of the number and percentage of errors or requirement violations within a data set or across data sets
- Data Quality Trends: 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: As-is and roadmap for expansion
