Data Analytics: What Recruiters Can Learn From Sports

For a wide range of organizations, data-driven decision-making is the key to success. In particular, the world of sports – including the MLB, the NFL, and the NBA – has embraced data analysis, leaving behind the days when decisions were made according to hunches. Now, coaches and managers regularly use data analysis to see which players are truly helping them win.

Businesses can learn a lot from these sports organizations when it comes to recruiting new talent. Many similarities exist between the process of evaluating athletes and the process of evaluating job applicant.

How Data Changed Sports – and Can Change Recruiting, Too

In The Atlantic, Terrance F. Ross describes how things have changed in sports:

"Once, the dominant way of judging how well a player or team would perform was the 'eye-test' – the organic, gut-instinct impression that came simply from watching a game unfold. But that time has been replaced by an era in which coaches and their backroom staff pore over formulas and figures – how many mid-range jump shots a team uses versus attempts near the hoop, or how many three-point shots versus two-pointers – to predict the most effective methods for winning."

The "eye-test" happens to perfectly describe how recruitment has traditionally worked. Recruiting and hiring decisions have always been subjective to some extent, but they don't have to be a crapshoot. Data analytics can yield valuable insights about whether or not someone is suitable for a given job. Recruiters have resume databases, personality evaluations, scientific aptitude tests, and a variety of other measurements at their fingertips. These sources make data analytics a perfect and easily implemented addition to the recruitment tool kit.

Data analysis offers a window into a candidate's potential by examining facts about past employee performance. For example, say an open job involves making tight deadlines and working well with a team. Data analysis can look at the performance, personalities, and backgrounds of employees who held the role in the past, both successful and unsuccessful. From there, recruiters and hiring managers can get a good idea of what kind of person tends to be most successful in the role. They can then use this information to make better predictions about the candidates they meet with.

Data Helps – But It's Not Perfect

Though game-changing, data isn't a crystal ball. It can provide deep insight into the potential value of an individual, but it can't tell the whole story.

For example, could data alone tell us that the Golden State Warriors would win a record 73 regular season games but lose in the finals? Or that the Cleveland Cavaliers would be the first team in finals history to fall behind 1-3 but win the series? Or how close LeBron James is to overtaking Michael Jordan as the best player of all time? No, no, and no.

Similarly, data can't guarantee how a new recruit will handle any pressures that aren't reflected in the data. There will always be an unpredictable element to both sports and recruiting.

Before adopting data analytics, businesses should recognize that data isn't meant to provide a complete picture of a candidate. Rather, it is meant to create a fuller, more fact-based picture. Just as a basketball coach wouldn't rule out an obviously terrific player just because the data seemed to point in another direction, recruiters shouldn't totally abandon the eye-test. There's still no substitute for judgment. Data simply provides a foundation that makes judgments more reliable, and it's up to recruiters to build on that foundation.

The data also facilitates an ongoing assessment of its own value. Predictive data can and should be modified over time based on results.

Just as sports teams that shun data have become rare, recruiters without data analysis skills will soon be an endangered species. Savvy recruiters will find that data analytics powerfully complement traditional recruitment methods by enhancing their judgement. The human element is empowered, not diminished, by data analytics.

Dash Davidson is a senior data analyst at Tableau.