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Environment Management

C2

Manage the environments for all solution delivery activities.

Improvement Planning

Practices-Outcomes-Metrics (POM)

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

2Basic
  • Practice
    Define a standard production environment.
    Outcome
    A standard production environment is defined.
    Metric
    Existence of a defined production environment.
  • Practice
    Test solutions using data obtained from production, as required.
    Outcome
    Problems found by testing better match the potential problems in the live system.
    Metrics
    • # of data-related problems found after roll-out.
    • # of hours used to generate test data.
3Intermediate
  • Practice
    Implement environments on virtualized infrastructure (e.g. cloud, virtual machines).
    Outcome
    A virtualized infrastructure is in place.
    Metric
    Time required to stage a specific environment.
  • Practices
    • Ensure that cleaned and anonymized cloned production data is used and data access is controlled.
    • Put in place reference (gold) copies of data.
    Outcome
    Efficiency is increased.
    Metrics
    • # of data-related problems found after roll-out.
    • # of hours used to generate test data.
4Advanced
  • Practices
    • Implement replica environments on a virtualized infrastructure.
    • Ensure that self provisioning of production-like test environments is in place.
    • Put in place fully repeatable roll-forward and roll-back methods.
    Outcomes
    • Data integrity is maintained.
    • Being able to self-provision environments reduces test time.
    Metrics
    • # of replica environments that are not self provisioned.
    • # of environments provisioned (tests deployed and torn down).
  • Practices
    • Ensure that there is a system in place to generate new data suitable to the test environment.
    • Ensure that test suites seamlessly integrate with refreshed data.
    Outcome
    For new systems with little or no live data, this will help identify problem at an early stage.
    Metrics
    • # of data-related problems found after roll-out.
    • # of hours used to generate test data.
5Optimized
  • Practices
    • Implement multiple types of environments that can be automatically provisioned, and supported.
    • Ensure that definitions of environments are solution-led, depending on specific needs.
    Outcome
    Data integrity is maintained with maximum flexibility.
    Metrics
    • # of replica environments that are not self provisioned.
    • # of environments provisioned (tests deployed and torn down).
  • Practices
    • Ensure that there is an overall master data strategy in place, part of which includes the provision of test data.
    • Where appropriate, implement advanced techniques, such as artificial intelligence (AI).
    Outcome
    Data usefulness is improved, and generated data is normalized to the environment.
    Metrics
    • # of data-related problems found after roll-out.
    • # of hours used to generate test data.