How to Approach a Data Intensive Problem

A reason for this precaution is that without a good understanding anything can be explained by the data. The larger amount of data you have, the greater are the misconclusions it allows you to make! The traditional statistical approach is to have a predefined hypothesis that is either approved or rejected with the evidence from data. The machine learning camp is bit more relaxed, but still you need to have separated sets of data for training and testing the model. So, what can you do if you only have a faint idea about the problem and no real data at all?