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Representative POMs are described for Metadata Management at each level of maturity.

2Basic

• Practice

  Gather metadata based on defined schema, structures, or specific data dictionaries.

  Outcome

  Data structures meet the immediate need and standard approaches to acquisition are taken.

  Metric

  % of data sources for which un/structured metadata acquisition is manual.

• Practice

  Develop application-specific data dictionaries.

  Outcome

  Quality improvements and better use of data are evident in well documented areas.

  Metric

  % of data sources for which metadata based on a dictionary schema exists.

3Intermediate

• Practice

  Augment metadata acquisition by automated tools where possible.

  Outcome

  Data structure containers can be populated automatically in some cases.

  Metric

  % of data sources for which un/structured metadata acquisition is partly-automated.

• Practice

  Rationalize metadata into a single master metadata repository for core business functions.

  Outcome

  Business and technical metadata models enable better use of higher quality data for both IT and business stakeholders.

  Metric

  % of data sources for which metadata has been standardized and moved into a central master repository.

• Practice

  Develop expertise on metadata and data modelling across the business and ensure all work together.

  Outcome

  Expertise is developing in both the business and in IT to enable effective communication and cooperative working.

  Metric

  % of business and IT employees who have received relevant training and/or shared expertise in other ways.

4Advanced

• Practice

  Automate metadata acquisition extensively.

  Outcome

  Data structure containers can be populated automatically in most cases.

  Metric

  % of data sources for which un/structured metadata acquisition is fully-automated.

• Practice

  Agree and ensure the extensibility of metadata architectures.

  Outcome

  Rich descriptive metadata enables data exchanges that are both internal and external to the organization and that can be quickly and efficiently put together.

  Metric

  % of data sources for which metadata and their interdependencies are mapped to the business processes.

• Practice

  Map metadata to business processes for context and identify dependencies between metadata.

  Outcome

  Rich descriptive metadata enables data exchanges that are both internal and external to the organization and that can be quickly and efficiently put together to respond to business needs currently and guide future developments.

  Metric

  % of data sources for which metadata and their interdependencies are mapped to the business processes.

5Optimized

• Practice

  Fully automate metadata acquisition.

  Outcome

  The risks from change are managed in relation to the full impact of changes and the implementation and deployment requirements.

  Metric

  % of data sources for which automated, structured metadata acquisition is mapped back to business processes.

• Practice

  Develop data acquisition tools to automatically map metadata back to business processes and to identify metadata dependencies.

  Outcome

  Subject mater experts are relied on less and documented metadata is the source of knowledge about data.

  Metric

  % of data sources for which automated, structured metadata acquisition is mapped back to business processes.

• Practice

  Use metadata to model the impact of proposed changes.

  Outcome

  The impact of business changes can be properly costed and ROI completed.

  Metric

  % of data sources for which metadata can be wholly relied upon to inform change control management.