You may have business rule ‘messes’ in your transaction systems. I’m referring to business rules created by well-meaning IT folks and Business Analysts, attempting to direct a complex decision using linear business rules. This pretends to be data science but is often a ‘mess’. The overzealous use of rules posing for data science is a common situation. Many times these rules are worse than doing nothing (guessing), as far as supporting a complex decision. Worse, they lead to poor data (to much data entry required to make them work as planned). Worst of all, they may be encoding a linear thinking and a bias towards ‘averages’ rather than distributions, when it comes time to interpret heuristics into data science.
The ‘mess’, may be a good place for a new data science project. Likely you will need to rip out the rules altogether (not popular with IT). However, assuming the data is semi-clean, the historical use of the business rules may prove to be useful predictors in a multi-variate model. Not all business rules are a mess if they are the result of simple heuristics and have been maintained properly. In any case, look for these pseudo models as a way to improve decision making with true analytics.
Edward H. Vandenberg