| | November 201619CIOReviewThere is a fascinating collection of charts that have been shared across social media over the last year and half. Designed to look like subway maps, colorful lines snake around a page where metro stations represent the different skill sets a data scientist must master in order to be successful. It is, at the same time, an impressive and frightening amount of knowledge to command. And despite its expansiveness, the chart is not complete.The chart leaves off intuition. In our rush to produce "skill ready" analysts and data scientists to fill the empty positions at companies, we've forgotten the art of research. It is analogous to trying to create more authors by just teaching grammar and spelling but not exploring the vital role of imagination and storytelling. Analytics and Data Science, in a world that now understands the value of all the information it stores, cannot simply revert to regurgitating information pulled from large databases. Without intuition to help drive the research and data discovery process, we provide a disservice to the decision makers we aim to help by not properly converting data and metrics into actionable intelligence or insight. Numbers can be illuminating but, provided in a vacuum with no context, are not useful and serve to undermine the premise that data can play an integral role in any decision. By passing data and metrics through an intuition filter, we increase the value and relevance of the information.Intuition is that gut-feeling, that hunch, that subconscious reasoning that drives us to make connections that we don't have evidence or analytic data to support. Intuition plays a critical role in many stages of data analysis and insight development. While it would appear that a numerically driven process and a subconscious process should be diametrically opposed, you will find that the most successful "quants" live and work in the space between intuition and data. So, how do they not just coexist but support each other? To understand, we first must explore the different stages of business research. From the moment a project begins, at the definition and hypotheses formulation stage, intuition provides the over-Off the ChartsBy David Greenberg, VP-Business Intelligence & Data Analytics, Higher Onearching direction. Imagine, if among the terabytes of information to sift through, an analyst charged with finding interesting connections or answers to a problem, had no idea of where to start first. To reuse my earlier analogy, it's the data science equivalent of a writer staring at a blank page without an inkling of what story to tell. Where to begin? Crunching through data at random hoping something useful might fall out would be a colossal waste of time and energy.David GreenbergCXO INSIGHTS
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