With many businesses looking to use big data analytics, it’s more important than ever to find people who know how to use big data. This has proven to be a notable challenge, in particular because of a major big data skills gap. Companies have a high demand for people with analytics skills, but the number of people who actually have those skills is low. It’s a problem that doesn’t appear to be going away any time soon. But simply finding somebody with a talent for big data may not be enough; in some cases, executives and managers may be spending more time looking for a wide variety of talents than they probably should. The key to better use of big data analytics along with having a more versatile workforce may be in analytic athleticism.
Analytic athleticism is a phrase first coined by Bill Franks of Teradata. The concept revolves around the idea that people with an inherent ability to work with statistics and data can apply those skills in various ways, much like someone with inherent athletic ability can showcase their own talents in multiple sports. If somebody has the ability, that ability should be explored to the fullest extent while making sure those talents are up to date.
Big data analytics is an area that has seen rapid changes over just the past few years. And because the pace of the evolution of analytics isn’t expected to slow, a person who possesses analytic athleticism can demonstrate tremendous value to an organization. Unfortunately, some organizations believe it is necessary to hire many different people to cover as many analytics skills as possible. One person may be hired for their expertise in machine learning algorithms, while another is hired for their skill in a programming language like Python. Having a diverse array of talent isn’t necessarily a bad thing, but it is likely not the most efficient use of resources to achieve business goals with big data. Cultivating analytic athleticism, however, can best achieve that goal.
In the world of big data analytics, there are many different skill sets.
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