An important part of business analytics / OR practice is to assess the impact of a prediction-driven decision-support (PDS) system on:
a) the end-users aka consumers
Data-driven analytical prescriptions are based on perturbing a predictive model, which in turn is (usually) based on the observed collective consumer response to the same or similar products offered in the past. If the PDS recommends a clearly obvious pattern of decisions that differ from the past, it can change the behavior of even non-savvy customers, the cascading effects of which can be disruptive to the product provider's business. With all these mobile apps, the population of non-savvy customers is shrinking every day. Therefore, being proactive in designing the PDS to account for this feedback can be important.
b) the PDS
Some times, the decisions that the PDS recommends today (based on yesterday's history), become part of tomorrow's history, which in turn drives the predictor. However, we can be proactive in designing a PDS that is more likely to 'make' a friendlier history down the line. Furthermore, the 'inventory' of history you need to stock up on to calibrate your predictor can also be minimized. History, like gasoline, is often a scarce resource in OR practice.