Finding root cause for low key performance indicators

A computer system for identifying a change of an indicator value for a system of interdependent entities, the computer system comprising:

• a processor set;
• and
• a computer readable storage medium;
• wherein:
• the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium;
• and
• the program instructions which, when executed by the processor set, cause the processor set to identify the change by:
• determining the indicator value;
• logging input data for the system of interdependent entities;
• logging scoring payload data and related results of a machine-learning model used as part of the system of interdependent entities, wherein the scoring payload data are derived from the input data;
• clustering the input data into a number of clusters resulting in defined clusters;
• re-determining the indicator value for each defined cluster, resulting in a vector of re-calculated indicator values;
• calculating metric values of the machine-learning model for each defined cluster, resulting in a metric values vector;
• and
• determining correlation matrix values for a correlation matrix between the vector of re-determined indicator values and the metric values vector of the defined clusters.

A computer program product comprising a computer-readable storage medium having a set of instructions stored therein which, when executed by a processor, causes the processor to identify a change of an indicator value for a system of interdependent entities by:

• determining the indicator value;
• logging input data for the system of interdependent entities;
• logging scoring payload data and related results of a machine-learning model used as part of the system of interdependent entities, wherein the scoring payload data are derived from the input data;
• clustering the input data into a number of clusters resulting in defined clusters;
• re-determining the indicator value for each defined cluster, resulting in a vector of re-calculated indicator values;
• calculating metric values of the machine-learning model for each defined cluster, resulting in a metric values vector;
• and
• determining correlation matrix values for a correlation matrix between the vector of re-determined indicator values and the metric values vector of the defined clusters.