KPIs underlie several statistical, procedural and conceptual properties and requirements.

From a statistical point of view a KPI (e.g., profitability of a company during a particular period) must be measurable. In other words, sufficient and proper data has to be available to develop a causal model with the KPI constituting the dependent variable. It is insufficient to look at the 'performance' of KPIs alone. To reflect the complex reality, a causal model has to comprise all possible factors expected to contribute - positively or negatively - on a given KPI.

The type and quality of data is another very important prerequisite. The more sound and reliable the data for any predictor or independent variable (i.e., a variable which is hypothesized to influence the KPI directly or indirectly) is, the more reliable and accurate the causal model will be.

Several other conceptual and mathematical properties have to be met before a complex causal model can be arrived at and before any factor can be estimated in its effect size on a given KPI.

The quality of the data as well as the fit of the causal model are essential to measure the effects on a particular KPI. Causal modelling is a very demanding process due to its complexity. For this, a great deal of expertise and much hands-on experience is required. But the financial benefits for your company or organisation are huge!

While our innovative 4M Technology is bound to resolve the complexity of causal modelling, a close cooperation between our experts and your specialist staff will pave the road for a rewarding outcome!