Standard data quality measurement processes give consistent results across cloud and on-premises environments; the metrics produced are integrated with relevant metadata and are transparent, traceable and auditable; measurement runs in a timely, accurate and consistent way throughout the lifecycle; the scalability and efficiency of measurement are reviewed regularly; and roles and responsibilities, defined with data owners, are communicated to stakeholders. Measurement covers data at rest (periodic, non-blocking) and data in motion (within production processes, possibly blocking publication), with control points placed deliberately near source and near consumption, all data forms including semi-structured and unstructured, and operational metadata that tracks coverage, cost, status and traceability from rules to outputs. The organization chooses a balance of central and federated measurement and sets SLAs and SLOs for the measurement service.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.