Using Big Data to Make Better Hiring Decisions

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18 February 2015

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The book Moneyball and the Hollywood movie based upon that book have become quite famous.  In 2002, Oakland A’s General Manager Billy Beane found himself struggling to rebuild his team with the lowest salary constraints in baseball. In order to overcome this obstacle, he had to think creatively about choosing new players. He turned to statistical analysis, using past data to predict future performance. Today, this type of analysis is common in all professional sports, but it’s slowly making its way into corporate talent management practices as well. Big data is being deployed across the country to help HR teams make better hiring decisions.

The Limitations of Traditional Decision Making

Traditional hiring models rely heavily on the internal HR department. While the HR team is certainly well versed in how to conduct interviews and manage a hiring process, they don’t always know what to look for in individual candidates, especially when it comes to high-tech or high-skill positions. Department managers can give HR teams a guide, complete with a list of skills and keywords, but the average HR associate doesn’t know how to effectively evaluate the skills of a developer or systems architect in a personal interview.

Conversely, department managers and supervisors may know what to look for in a candidate from a skills perspective, but they aren’t well versed in the more subtle aspects of interviewing. They aren’t practiced in the finer points of a behavioral interview or matching a candidate’s personality to the company culture, for example.

The Objectivity of Big Data

One way that companies are overcoming the human boundaries of the hiring process is through the use of big data.  By analyzing data and removing the human element from certain steps of the hiring process, companies are limiting the bias that often crops up in the candidate screening process. For example, human resources professionals have been taught for years to avoid candidates who have “too many” jobs on their resumes. Job hopping, as the traditional mode of thinking goes, is a sign of laziness or poor work ethic. However, a study of more than 20,000 workers by Evolv recently revealed that prior employment tenure had no bearing on predicting future success. That means that many candidates who job hop have truly not found the right opportunity, and therefore should not automatically be disregarded in the screening process.

There is an almost endless array of ways in which companies can use big data to help improve hiring decisions. It can be deployed to test and predict work ethic, tenure, personality match, identify candidates with leadership potential, and can even predict long-term success.

While following gut feelings and assumptions about candidates can lead to poor hiring decisions, it does not mean that statistical data should replace humans entirely when it comes to the hiring process. At the end of the day, employers want to hire people that they genuinely like and who they believe in as a candidate. However, big data and analytics can be strategically deployed to help eliminate emotion from the process, reduce bias and assumptions and increase the chances that a hiring manager’s final decision is the best hiring decision for the team and the organization as a whole.

If you are a big data or analytics professional looking for new opportunities to grow your career contact OnBoard Recruitment Advisers today. We specialize in analytics and big data positions in a variety of fields, and we work with some of the most innovative companies in the country. We look forward to helping you take the next step in your career.

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