Data mining, the process of identifying patterns and structures in the data, has clear potential to identify prescriptions for success but its wide implementation fails systematically.
Companies tend to deploy ‘unsupervised-learning’ algorithms in pursuit of predictive metrics, but this automated [black box] approach results in linking multiple low-information metrics in theories that turn out to be improbably complex.
The article below points out the pityfalls
When it comes to data, size isn’t everything because big data on their own cannot just solve the problem of ‘insight’ (i.e. inferring what is going on). The true enablers are the data-scientists and statisticians who have been obsessed for more than two centuries to understand the world through data and what traps lie in wait during this exercise. In the world of analytics (AaaS), it is agility (using science, investigative skills, appropriate technology), trust (to solve the client’s real business problems and build collateral), and ‘know-how’ (to extract intelligence hidden in the data) that are the prime ‘assets’ for competing, not the size of the data. Big data are certainly here but big insights have yet to arrive.