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Representative POMs are described for Capacity and Variety Management at each level of maturity.

2Basic

• Practice

  Collect and document the current capacity of data analytics and the variations in data types used.

  Outcome

  Current capacity and data type variations are generally understood and documented.

  Metrics

  • Count of data types used for analytics.
  • Percentage of the data analytics resources that are documented and understood.

3Intermediate

• Practice

  Put in place a process to manage the capacity required for data analytics, based on projected business volumes, forecasted business requirements, and fluctuations in the data types.

  Outcome

  Current and future capacities for data analytics are managed for a range of business volumes, business requirements, and data type fluctuations.

  Metrics

  • Current vs planned capacity for data analytics.
  • Percent accuracy of forecasted data analytics requirements/year.

4Advanced

• Practice

  Use advanced techniques and processes so that data analytics can quickly react to changes in the volume of work and/or the complexity and variety of tasks required to meet on-going business needs.

  Outcome

  Data analytics capacity is highly adaptive and is planned based on changing business requirements and forecasts.

  Metrics

  • Count of changes to requirements/forecasts successfully handled by data analytics.
  • Percentage change in the capacity of the resources for data analytics year-on-year.

5Optimized

• Practice

  Use feedback from across the organization together with the latest academic, vendor, and industry research to continually improve the capacity management of data analytics.

  Outcome

  The management of capacity and data variety is continually improved based on feedback and research.

  Metrics

  • Percentage improvement in the accuracy of data analytics forecasting.
  • Percentage improvement in the utilization of data analytics resources.