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Hiring based off of data

10th Sep 2013
Outreach Coordinator Distilled Creative
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Despite the recent economic slump, job abandonment rates in the private sector haven't witnessed a noticeable decline. Considering the expenses associated with screening and training new team members, employers must always focus on hiring individuals that have long-term potential. However, this is difficult to predict using traditional methods. Thankfully, we are living in the age of the algorithm.

Big data has emerged as a powerful tool for filling employment rosters with highly skilled individuals. Unlike a human recruiter, big data analytics software is capable of scanning millions of employees simultaneously. Because of this, companies are using algorithms to mine large amounts of data and locate hidden candidates that possess untapped talent.

Algorithmic Hiring

Algorithmic hiring focuses on merit and is indifferent to race, gender and sexual preference. The absence of commonly held bias significantly increases the odds of creating an applicant pool that isn't overly expensive.

Crunching big data offers incredible insight on the qualities associated with a good hire. Seemingly odd metrics have the potential to tell a lot about a person. For example, the choice of an Internet browser can reveal how likely a potential employee is of long-term success. While this sounds absurd at first, the science behind it is surprisingly sound.

Death of the Resume?

Big data is actively making traditional hiring practices look outdated and hugely cumbersome. As reported by the New York Times, evidence has emerged that's turned the world of recruitment on its head. Instinctual assumptions about what makes a good employee appear to be out of touch with reality. Factors like previous work history and college GPA have been revealed to be almost useless in predicting the likelihood of an employee achieving success.

Focusing exclusively on professional and academic credentials is standard practice, but experts insist that this causes companies to miss out on a wealth of talent. It's reported that for every employee found through traditional recruitment methods, at least 100 equally qualified individuals are being passed over.

How Big Data Increases the Talent Pool

Algorithms allow employers to locate self-taught individuals that are extremely talented. In fact, a significant number of Google employees have never attended college. When one considers the fact that Google has dedicated an entire department to people analytics, it becomes easy to understand how big data is changing workforce science.

Algorithmic hiring vastly increases the talent pool by locating top-notch skill in places that traditional recruiters wouldn't even think about looking. For example, recruitment software now pinpoints sites that a specific target demographic is likely to hangout. This allows recruiters to attract quality individuals that weren't actively pursuing a new job.

Importance of the Human Element

The majority of employers already use algorithms to pre-screen potential job candidates, and the role of big data appears destined to increase. Still, many are concerned that big data analytics will eventually replace human judgment entirely.

Big data is naturally complex, and certain algorithm factors are often beyond our grasp. For example, Google is capable of predicting flu patterns based on search habits and user information. However, even the designers of this project can't fully explain why it works.

This causes one to ask a fundamental question. Can bots be trusted to make crucial decisions involving human beings? Although technology allows big data to be processed more efficiently than ever, it's important to understand that even the best algorithms require a human touch.

Remember, data is useless without proper context. If employers forget this fact, they run the risk of having cookie-cutter teams that lack the originality required for game-changing innovation. Although big data is incredibly useful for recruitment purposes, it would be unwise to automate the hiring process completely.

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