Data quality is measured and judged in a systematic way, using defined processes and techniques and testing the data against its quality rules. Assessments run on a regular cycle at the frequency set in the approved assessment policy, and data governance decides, for each subject area, which attributes matter most and which quality rules apply. Findings come with remediation recommendations and the reasoning behind them. Each chosen quality dimension is judged against targets and thresholds set in advance. Measurement reports are produced routinely, driven by how critical and how volatile the attributes are. The assessment and reporting processes themselves are reviewed and improved on a continuing basis.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.