By implementing algorithms that are able to learn from the data that they explore, machine learning technologies already outperform traditional analytics by far. (No wonder high-flying companies like Google, LinkedIn, Amazon and Pandora have built their businesses around it.)
The key is the ability of machines to independently assess patterns and outcomes across far wider data sets than traditional analytics tools ever could. This obviates the need for time-intensive manual processing, and allows companies to fully exploit data collection techniques, employ cheaper storage, computing power and distributed database technologies — all of which are vital in an era where data doubles every two years.
In spite of such advantages, the very notion of machine learning still tends to trigger primal fear and mistrust among those who see the removal of human analysis from the equation as the first step to their irrelevance. So when exploring the potential of implementing…
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