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

1Initial

• Practices

  • Manually acquire meta data for specific purposes.
  • Engage and allow subject matter experts to use meta data where necessary.

  Outcomes

  • Metadata is mostly not present.
  • Data structures may not meet the immediate need, and are inefficient, inconsistent and lack the ability to cater for future requirements.
  • Basic data structures may be inefficient or facilitate duplication of data without the means to synchronize updates.

  Metrics

  • % of data sources for which unstructured meta data acquisition is manual.
  • % of data sources for which meta data exists.
  • % of KPIs unanimously defined.

2Basic

• Practices

  • Gather meta data based on defined schema, structures or specific data dictionaries.
  • Develop application specific data dictionaries and widely apply them.

  Outcomes

  • Data structures meet the immediate need and standard approaches to acquisition are taken.
  • Quality improvements and better use of data is evident in well documented areas.

  Metrics

  • % of data sources for which un/structured meta data acquisition is manual.
  • % of data sources for which meta data based on a dictionary schema exists.

3Intermediate

• Practices

  • Augment meta data acquisition by automated tools where possible.
  • Rationalize metadata into a single master meta data repository for core business functions.
  • Develop expertise on meta data and data modelling across the business and ensure all work together.

  Outcomes

  • Data structure containers can be populated automatically in some cases.
  • Business and technical meta data models enable better use of higher quality data for both IT and business stakeholders.
  • Expertise is developing by both business and IT.

  Metrics

  • % of data sources for which un/structured meta data acquisition is partly-automated.
  • % of data sources for which meta data has been standardised and moved into a central master repository.

4Advanced

• Practices

  • Extensively automate meta data acquisition.
  • Agree and ensure the extensibility of meta data architectures.
  • Map meta data to business processes for context.
  • Identify dependencies between meta data.

  Outcomes

  • Data structure containers can be populated automatically in most cases.
  • Rich descriptive meta data enables data exchanges both internal and external to the organization that can be quickly and efficiently put together.

  Metrics

  • % of data sources for which un/structured meta data acquisition is fully-automated.
  • % of data sources for which meta data, and their inter-dependencies are mapped to the business processes

5Optimized

• Practices

  • Fully automate meta data acquisition.
  • Develop data acquisition tools to automatically map meta data back to business processes and to identify meta data dependencies.
  • Use metadata to model the impact of proposed changes.

  Outcomes

  • The risks from change are managed in terms of understanding the full impact of changes and the implementation and deployment requirements.
  • Subject mater experts are relied on less and documented meta data is the source of knowledge about data.

  Metrics

  • % of data sources for which automated, structured meta data acquisition is mapped back to business processes.
  • % of data sources for which meta data can be wholly relied upon to inform change control management