#Asia The big data balance: a man-plus-machine approach to hiring


Making data and people analytics work for your recruitment strategy comes down to combining the intuition of man with the accuracy of technology

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As human beings, our commonality is a precondition to choose people like us, favour similarities, and unconsciously make decisions that fit a flawed metric.

Consider these examples of our inability to remain unbiased:

If you are a criminal, hope you don’t get put before a judge on an empty stomach

According to research from 2011, judges gave more lenient decisions at the start of the day (after breakfast) and right after a scheduled break (such as lunch). In fact, the likelihood of a favourable ruling slumped from 65 per cent to zero between breaks or meals.

If you are a student, hope you don’t have a teacher who has low expectations of you

A study of 10th grade students between 2002 and 2012 by the Centre for American Progress found that students who had teachers with “higher expectations” were three times more likely to graduate from college. Teachers gave children who they expected to succeed more time to answer questions, more feedback, and higher approval. In fact, teacher expectations were a better indicator of college success than the student’s own motivation and effort.

If you are a job seeker, hope you don’t sit in front of a hiring manager who is a different gender, race or ethnicity than you

Recruiter bias has been well documented: from that 2009 study that used fake CVs to point out racism among some British recruiters, to the latest “expert” who suggests women need to lose the wedding ring in order to get hired (yes, this article is real… *eye roll*).

Also Read: Painless hiring: Gamified tech recruitment saves time and filters out unsuitable candidates

Humans are not perfect. Businesses have begun to recognise the need for alternate measures to help factor out bias like these examples, and help the company make strategic decisions based on fact. The answer? Technology.

The wider HR function is slowly but surely embracing the big data movement. A 2014 Towers Watson survey found HR data and analytics are the top three areas for HR technology spend, and 6,400 companies with 100 staff or more will have implemented big data analytics by 2018. In case you didn’t realise, that’s just over one year away.

From a recruitment point of view, data is king. People analytics is helping companies pick the perfect talent out of a virtual haystack. The data desire is so strong that Yahoo! Bought HotJobs; Microsoft purchased LinkedIn; Indeed bought SimplyHired and, more recently, Randstad Holdings made the move to acquire Monster Worldwide for a rather large US$429 million. Big data is big business.

The deal with data

Today, talent acquisition specialists have access to more open information than ever, thanks to social media and an array of web-based applications that allow us to gather data.

Data-based recruiting, more or less, works across a technology spectrum which:

• Rakes the internet for details and information that pinpoints desirable candidates. This could be via Facebook, LinkedIn or other specialist industry social sites like Github.

• Uses data aggregation software developed in-house, where thousands of resumes and bucketloads of gathered information are arranged in an ever-expanding database.

Also Read: What trial and error taught me about my hiring model

Some companies use this data as a benchmark for assessing and analysing candidates through test, quizzes and games, while others might use it to round up a group of potential talent based on preferences and parameters set through a platform like TalentDash.

From aggregation to algorithms

Once all the required information has been gathered, algorithms come into play. These calculations are not simply about crunching together masses of data willy-nilly – it’s about using keywords and scores to identify key aspects of the data, and interpret what the algorithms produce into useable, real information.

The final results might be displayed as various matches, patterns or perhaps easily-digested visual heat maps, like this.

Of course, data – and recruitment technology – is not the be-all and end-all to our hiring limitations. Humans, although flawed, are a necessary part of the equation.

Real data is an incredible addition to any hiring strategy, but skilled people are needed to ensure the information being presented is understood and utilised in the most effective way possible.

At the end of the day, people analytics is precisely that – utilising people and data. Combining man and machine to mine your way to the right candidate.

The human element is the last piece of the data puzzle to help businesses make connections and choose the best candidates – but the technology is necessary to ensure we flawed beings can fine-tune our approach and ultimately make correct decisions based on accurate metrics and benchmarks.

The article was first published on TalentDash.

The views expressed here are of the author’s, and e27 may not necessarily subscribe to them. e27 invites members from Asia’s tech industry and startup community to share their honest opinions and expert knowledge with our readers. If you are interested in sharing your point of view, submit your article here.

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