The enterprise puts in place data profiling methods, processes, practices, tools and templates that are standard and usable across many repositories and stores. Because they are defined once and standardised, profiling work can be reused and shared between data stores. Data management names the central, shared data sets that are profiled and watched on a regular basis. Profiling checks whether data content matches the approved metadata and standards. Issues actually found are set against the issues that statistics from past results would predict. Profiling results are kept in one central place and are tracked and analysed against statistics and metrics to guide quality improvement. Automated profiling reports, produced in real time or close to it, cover every critical data feed and repository.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.