What is Analytic Athleticism and Why is it Important?

by   |   December 30, 2016 5:30 am   |   0 Comments

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. From different analytics techniques, to platforms, and even to tools, there are a large number of talents that should be kept in mind. The idea that Franks puts forward is that someone with keen data ability can expand their skill set to include more platforms, tools, etc. Much like a talented football player can transition his skills into a track & field competition, someone who knows big data really well wouldn’t have to stretch too far to learn the latest big data techniques. In other words, hiring a new employee to cover the new technique wouldn’t be necessary; the talented existing employee would be able to handle it with enough time to learn and train. If the employee already knew Apache Hadoop, for example, it’s well within the realm of possibility for that employee to become an expert in Spark as a Service.

It’s important to note that analytic athleticism is something that can be applied on a personal level as well as an organizational level. An individual may show analytic athleticism on their own, making sure they are always on the lookout for new talents to incorporate into their own skill set. The more they can offer a prospective employer, particularly when it comes to providing solutions, the more likely they will land a good job. So providing big data, infrastructure, or modular solutions is a big deal for the employer. At the organizational level, the business needs to ensure that its level of analytic athleticism is high, giving employees across the company the opportunity to practice new skills. It also begins with hiring the right talent in the first place, and that requires being able to identify who has analytic athleticism early on. Much like scouts for sports team, if you can find that person with inherent ability, the organization will be much better for it.

Analytic athleticism may be a new idea, but it builds off of the already established concept of growing one’s skill set. Cultivating that talent can lead to big dividends in the future, especially since finding that singular skilled individual is such a challenge for most businesses.


Rick Delgado is a technology commentator and freelance writer.


Subscribe to Data Informed for the latest information and news on big data and analytics for the enterprise.

Tags: , ,

Post a Comment

Your email is never published nor shared. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>