Predicting ROI from a data quality program (and many other business initiatives) is like predicting the weather. Probably you are able to guess if it is going to be good or bad, but most often you don’t exactly guess how well or bad it actually turned out.
Chances for predicting the weather right varies along with the time of year and your location. I have the pleasure of living in a place (Denmark) where the weather is pretty unpredictable.
Well, winter is usually cold and summer is warm.
We also know that if we have easterly winds coming in from the Russian Steppe during winter, it turns very cold. In summer that wind will make beautiful hot sunny days. Westerly winds in the winter coming in from the Atlantic Ocean means temperatures above freezing. In summer that wind often has some chill and rain with it.
But these are the main scenarios. Between those rough generalizations there is a myriad of factors, events and not fully understood processes that makes weather forecasting a chaotic discipline.
Making business cases for data quality programs have the same challenges. Well, at some spots on the globe (in some parts of the year) you can wake up every morning and be certain that it is going to be a hot sunny day. Likewise a lot of business activities will without any doubt benefit from better data quality – no further forecasting needed. In other cases it may be uncertain. Here you may rely on previous experiences (case studies by others) and your position. You may outline a business case and you could be right.
This morning at my place was forecasted to be mostly cloudy but dry. It is damned cloudy and raining a bit.