Analytics has an overwhelming power and strength. Despite that it has a deadly vulnerability - very much like the Death Star. It has happened to us all - a seemingly very good, accurate and elaborate model with pieces of true gold in it has gone to the oblivion and has never been appreciated in the organization. It took me couple of years to realize the reasons for that.
The ultimate goal for a model, no matter how complex and good, is to be a part for making one or more decisions. Decision processes could be good, bad or not present at all but they share a common limitation - ourselves and particularly our intrinsic political nature within organizations. No matter how much we praise technology, big data, scientific methods and complex algorithms, making a decision in an organization is a process involving social, political, psychological, organizational and even physiological factors. This all sums up in all sort of biases and agency "objectivity". I see that as the Achilles heel of analytics - the organizational decision processes and the lack of understanding how they work by the analytics provider. The reasons for failure or not achieving good results with analytics are hidden in these two reasons. A good model could be rejected because it does not provide a view that is good for a department, a group of managers or is in conflict of existing consensus. We have all witnesses cases when the model had to be distorted through implementation of biases and personal preferences to the point that we asked ourselves why do we need a model at all. Of course, this is the case when someone needs the authority of "the model" to reinforce his own statements.
A lot could be said about how to deal with these problems. I am not in the capacity to comment the decision processes in an organization. However in future articles I will share some ideas how to make analytics a success.