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.
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