It’s not the sort of issue many would lose sleep over. Yet one that would have the souls of many-a-technocrat go uneasy. What do technology-driven recruitment softwares have to do with hiring the right talent? Some would say that HR managers still make more logical decisions, irrespective of how true Moore’s Law has proven to be. Others would side with algorithms. One school of thought would have you believe that there is no better program written than the human genome that can predict the nature of job-seeking applicants and read more into the manner in which an interviewee does his necktie. The other obviously claims that “Big data” can spot talent – having learnt from reading millions of data points in the past – and therefore the recruitment program on your server is a safer option.
Common belief. HR representatives err. And why would we doubt the potential of bug-free, programmed-to-perfection recruitment softwares like SAP’s SuccessFactors, Oracle’s Taleo, SilkRoad's OpenHire, et al? A quick look at some recent researches however, makes us doubt whether such softwares even make the grade when it comes to helping companies hire competent workers. And what justifies our lack of conviction in big data? That these shocking findings are used in the very construction of many-a-logical assumption made by programmers of these software packages. Let us throw five of them at you. [The findings shared are those arrived at by studying “Over 2.5 million granular management and supervisory data points”, presented in an April 2013 joint report by Evolv Inc. and the Center for HR at the Wharton School of the University of Pennsylvania.]
Finding 1: Like to spend time at office on the social web? Big data says: “You’re a good hire!”
The first one is hard to digest for a Steve Jobs-like efficiency demanding boss. If you are an employee who spends time on anywhere up to four social networking websites during the course of a day at work or likes to keep himself engaged by downloading softwares (that did not come pre-installed with his/her work computer), then you will “Statistically” serve your employer better and longer! Big data says so. We “Humans” don’t. So get on with tweeting, tagging, posting, reworking your resume, updating your OS, and downloading pirated versions of films that will get screened across multiplexes a fortnight later…all while your customers wait in line for their cheese burgers and hotdogs. Who cares?
Finding 2: Changed 10 jobs in 3 years? Big data says” “You’re a good hire!”
Research has revealed that a candidate’s past job-hopping record should have no influence on his future performance at work and his tenure at the new employer. In short, give no preference to a person’s stable history at a company. Balderdash!
Finding 3: Lack experience? Big data says: “You’re a good hire!”
The third finding goes against conventional wisdom. HR officers would naturally attach some importance to past experience – in some cases maximum. And most descriptions of job openings state clearly the required minimum experience required for application to a job posting. Big data analysts report that previous experience is in no way related to either performance or tenure on the job. Conclusion – make fresh college passouts the CEOs of all Global Fortune 500 companies and soon we would have trillion-dollar corporations floating about a dime-a-dozen. No wishful talk this!
Finding 4: Spent time in prison? Big data says: “You’re a good hire!”
Strange. But big data says it is not. Data analysis shows that an employee’s criminal background has no influence on his/her output, sincerity or loyalty at work. Actually, hiring those with a criminal record means hiring employees who are better performers when it comes to “Customer-support”-related work profiles. Hire a criminal, and get your customer satisfaction levels up to levels never seen before. Killingly surprising!
Finding 5: Dishonest? Big data says: “You’re a good hire!”
In a study conducted at Xerox Corporation, more than 48,700 employees were interviewed in a 6-month-long process to find out the honesty quotient of the employees. It was discovered that those who fell in the “Dishonest” personality type, were better candidates (than the “Honest”lot) to be hired in the Sales & Marketing department! Shocking coincidence.
Problem with technology being used to hire talent is that big data often fails to distinguish between ‘signal’ and ‘noise’. Two cases that Prof. Prof. Peter Cappelli of Wharton has written about, illustrate the big problem. First, a Philadelphia-based HR executive told Prof. Capelli that he had applied anonymously for a job in his own company to test whether the hiring software was error free. He didn't make it through the screening process! Second was an email that Prof. Capelli received. A company received 25,000 applicants for an engineering position. The recruitment software concluded that not one candidate was qualified. Reason: none of the applicants had a certain title in their previous jobs. Why? The title was unique to the prospective employer! These underline well the problem with big data.
To expect softwares to choose the right job candidate (and therefore the right team) or the right product or the right market would be wishful thinking. Number crunching and providing indicative data tables and charts is fine. But irrespective of high the chance of an error with a human in charge, big data cannot and should not be used as an alternative to human expertise when it comes to final decision-making. Not today.
We can safely recommend that even advocates of big data should have the patience of a saint when it comes to recommending the replacement of human recruitment ‘decision-making’ officers with big data servers and PCs. They should hold their peace till the very failure rate of big data projects fall (from the current 45%, as per a survey by business-software firm Infochimps Inc.).
Our recommendation to big data advocates is – first allow big data to take care of the talent hunt for itself (demand for talent in this big data is expected to outstrip supply by 60% by 2018, as per The McKinsey Global Institute). Then we will bother HR specialists to pay more attention to big data robots. The Moneyball approach does not work each time. A hiring decision? Don't just leave that to big data. Not yet.