1. Data quality assessment, as a way for the practitioner to understand the scope of how poor data quality affects
the ways that the business processes are intended to run, and to develop a business case for data quality management;
2. Data quality measurement, in which the data quality analysts synthesize the results assessment and concentrate on the data elements that are deemed critical based on the selected business users’ needs. This leads to the definition of performance metrics that feed management reporting via data quality scorecards;
3. Integrating data quality into the application infrastructure, by way of integrating data requirements analysis across the organization and by engineering data quality into the system development life cycle;
4. Operational data quality improvement, where data stewardship procedures are used to manage identified data quality rules and conformance to acceptability thresholds;
5. Data quality incident management, which allows the data quality analysts to review the degree to which the data
does or does not meet the levels of acceptability, report, log, and track issues, and document the processes for
remediation and improvement.