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Representative POMs are described for Embedding Application-Specific Analytics at each level of maturity.

2Basic

• Practice

  Use automation when the problem is relatively straightforward — i.e. for tasks where there is a clear, predefined sequence of steps that can be transitioned from a human to a machine (for example, to filter out negative values).

  Outcome

  The speed and efficiency of data analytics is improved, and staff are freed up for more productive activities.

  Metric

  Number of manual data analytics steps that have been automated.

3Intermediate

• Practices

  • Use machine learning to determine how certain features of the data are related to the outcomes you might be interested in.
  • (Please note machine learning is suitable only for problems that require prediction rather than causal inference, and that are sufficiently self-contained or relatively insulated from outside influences).

  Outcome

  Machine learning eliminates guesswork by gathering and analysing large volumes of data to optimally leverage the resulting insights.

  Metrics

  • Number of insights gained by machine learning.
  • Value of insights gained from machine learning.

4Advanced

• Practice

  Decide where machine learning can be embedded, based on data suitability/quality and the tolerance for exceptions or statistical errors.

  Outcome

  Some embedded analytics are used and are rolled up to organizational data analytics.

  Metric

  Percentage of embedded machine learning used by the organization.

5Optimized

• Practice

  Use vendor research and internal feedback to build trust with vendors to both influence development and to provide completely embedded solutions.

  Outcome

  Embedded analytics are used exclusively in areas where trust in the vendor is justified.

  Metric

  Percentage of embedded (vendor provided) analytics.