The work of assessing the quality of data and managing data issues will not benefit the organization unless the information is shared through reporting so that data consumers understand the condition of the data. Reporting should focus around:
- Data Quality Scorecard, which provides a high-level view of the scores associated with various metrics, reported to different levels of the organization within established thresholds
- Data Quality Trends, which show over time how the quality of data is measured, and whether trending is up or down
- SLA Metrics, such as whether operational data quality staff diagnose and respond to data quality incidents in a timely manner
- Data Quality Issue Management, which monitors the status of issues and resolutions
- Conformance of the Data Quality team to governance policies
- Conformance of IT and business teams to Data Quality policies
- Positive Effects of improvement projects
Reporting should align to metrics in the data quality SLA as much as possible, so that the team’s goals are aligned with those of its customers. The Data Quality program should also report on the positive effects of improvement projects. It is best to do this in business terms to continually remind the organization of the direct effect that data has on customers.
