That's very well put reasons. But another significant reason is very often a highly accurate model is not the right model that an organization wants. Hence when

That's very well put reasons. But another significant reason is very often a highly accurate model is not the right model that an organization wants. Hence when you are taking this modelling exercise to the next level, the whole effort of modelling fails because you have spent your time on something that is not required. Hence the accuracy-sophistication obsession of data scientists - irrespective of they are leaders or just ICs - make it tough to establish their credibility and broader organization vision. And in many other cases, Media has created a myth of Data Scientists as Magicians, hence a lot of people expect a lot of unknowns from Data Scientists while a lot of people become Data Scientists always expecting to create unknowns and magics which is not going to yield any good better than a mere Exploratory Data Analysis based Insight from Microsoft Excel