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

1Initial

• Practice

  Collect data as needed.

  Outcome

  Increased likelihood of accurate model predictions for models fed with empirical data.

  Metrics

  • # of IT capacity empirical data input sources.
  • % of resources covered by IT capacity empirical data.
  • Frequency of IT capacity data collection.

• Practice

  Monitor the actual IT capacity headroom for selected resources.

  Outcome

  The actual IT capacity headroom of individual resources is understood and can be acted upon.

  Metric

  # of IT resources monitored for IT capacity headroom.

2Basic

• Practice

  Collect IT resource data on a regular basis.

  Outcome

  IT capacity modelling for some IT resources is based on up-to-date empirical data.

  Metrics

  • # of empirical data input sources.
  • % of resources covered by empirical data.
  • Frequency of data collection.

• Practice

  Use basic model change scenarios, for selected resources to drive forecasts.

  Outcome

  Capacity forecasting is informed by basic change scenarios for some resources.

  Metrics

  • # of IT capacity scenarios agreed with the rest of the business.
  • Frequency of IT capacity scenario review and refresh.

• Practice

  Use IT assumptions as the main input into the ongoing capacity modelling and planning.

  Outcome

  The IT capacity plans produced are largely based on IT driven assumptions and inputs.

  Metrics

  • # of IT capacity input assumptions.
  • Frequency of IT capacity input from IT.

3Intermediate

• Practice

  Implement a process to capture IT capacity empirical data for all modelled resources.

  Outcome

  the IT Capacity model accuracy is improved as it is based on a good level of empirical data.

  Metrics

  • # of IT capacity empirical data input sources.
  • % of resources covered by the IT capacity empirical data.
  • Frequency of IT capacity data collection.

• Practice

  Communicate the IT capacity data quality issues to data owner immediately.

  Outcome

  Increased the likelihood of accurate model predictions due to an increased quality of data inputs.

  Metrics

  • # of IT capacity empirical data input sources.
  • % of resources covered by the IT capacity empirical data.
  • Frequency of IT capacity data collection.
  • % of IT capacity inputs checked for quality.

• Practice

  Agree with the rest of the business on a set of scenarios to use to drive IT capacity forecasts.

  Outcome

  IT capacity forecasting is driven by defined and agreed scenarios.

  Metrics

  • # of IT capacity scenarios agreed with the business.
  • Frequency of IT capacity scenario review and refresh.

• Practice

  Operate a process for the collection of strategic direction, vision, business forecasts and operational objectives from the business for IT capacity planning.

  Outcome

  The IT capacity plans produced are informed by an ongoing flow of input from the business and are aligned to future business requirements.

  Metrics

  • # of IT capacity business input assumptions.
  • Frequency of IT capacity input from the business.

4Advanced

• Practice

  Establish a process to verify the quality of all the data used as input for the IT capacity model.

  Outcome

  There is an Increased likelihood of accurate IT capacity model predictions, due to increased quality of the data inputs.

  Metrics

  • # of IT capacity empirical data input sources.
  • % of resources covered by IT capacity empirical data.
  • Frequency of IT capacity data collection.
  • % of IT capacity inputs checked for quality.

• Practice

  Establish an escalation process for IT capacity data availability and data quality.

  Outcome

  There is an increased likelihood of accurate model predictions due to increased quality of IT capacity data inputs.

  Metrics

  • # of IT capacity empirical data input source.
  • % of resources covered by IT capacity empirical data.
  • Frequency of IT capacity data collection.
  • % of IT capacity inputs checked for quality.

• Practice

  Implement a process to establish and agree consistent scenarios for all IT capacity planning activities across different resources and business areas.

  Outcome

  IT capacity forecasting is driven by a consistent set of scenarios agreed with the business.

  Metrics

  • # of IT capacity scenarios agreed with the business.
  • Frequency of IT capacity scenario review and refresh.

• Practice

  Include macro trends and actual business usage patterns into IT capacity collection and input process.

  Outcome

  The IT capacity plans produced are calibrated to take account of any macro trends identified and actual usage patterns.

  Metrics

  • # of business macro trends included in IT capacity planning.
  • Frequency of IT capacity input from the business.

5Optimized

• Practice

  Refine and optimize the IT capacity data collection process continually with key stakeholders.

  Outcome

  The IT capacity models maintain accuracy with a continual feed of refined, optimized empirical data.

  Metrics

  • # of IT capacity empirical data input sources.
  • % of IT capacity resources covered by empirical data.
  • Frequency of IT capacity data collection.
  • % of IT capacity inputs checked for quality.

• Practice

  Optimize the IT capacity process for the collection and harmonisation of scenarios continually with key stakeholders.

  Outcome

  IT capacity forecasting is informed by a consistent set of scenarios that are continually reviewed and updated.

  Metrics

  • # of IT capacity scenarios agreed with the business.
  • Frequency of IT capacity scenario review and refresh.

• Practice

  Continuously refine and optimize the IT capacity collection process with key stakeholders and discuss the impact on model predictions with all key stakeholders.

  Outcome

  Capacity plans are accurately aligned to meet current and future business demands and requirements.

  Metrics

  • # of business input assumptions for IT capacity planning.
  • Frequency of input from the business for the IT capacity plan.