In the data mining process, where do data scientists like you and me add the most value?  Is it in exploring the data, uncovering anomalies and seeing relationships between elements?  In selecting transformations for the elements to improve their representations for modeling and analysis?  In building sophisticated predictive models?  For my money, the answer is ‘none of the above’.  I believe we contribute the greatest value by framing the problem.  We can do stellar technical work – but if the problem is framed poorly, it’s all a pointless exercise, maybe even solving the wrong problem.  Conversely, even a mediocre model - applied to the right, well-framed problem - will provide immediate benefit to an organization.    Read the full article