Hey Siri, who should we hire?
Towards an evidence based approach to HR. And can big data, AI and machine learning help?
Why don’t HR departments use more data and evidence based analytics for their recruiting , on-boarding, training, retaining and developing talent? For example, does your company have the data to identify robust predictors of a good manager? Google spent 10 years looking at the data and found ten attributes of great managers. Private companies like Hilton and the public sector agencies National Health Service in the UK demonstrate how institutions can streamline and optimize their hiring processes using data and evidence. Another example is Google’s rule of 4-four people separately interviewing each potential hire in a structured way resulting in an optimum hire with 86 percent confidence. This was structured through numerous iterations and tweaking the process. Current CEO Sundar Puchai was himself interviewed nine times by the company before first being hired.
But institutional inertia and unconscious biases seem to prevent large-scale adoption of these methods.
As Malcolm Gladwell points out, elite firms and institutions use elite colleges as their filtering tools for new hires. Gladwell quotes the late US Supreme Court Justice Antonin Scalia as saying he would never hire a clerk from Ohio State University. Well, it turns out that Scalia’s best clerk came from Ohio State. Like the Michael Lewis book Moneyball (about a down and out baseball team that turned to data and statistics for its team picks with astonishing success), firms are just starting to use data analytics to ferret out hidden gems in their hires (e.g. a blue collar job experience with good grades in law school point to future success).
Or take the case of increasing cognitive diversity. Dominic Cummings (advisor to Prime Minister Boris Johnson) famously issued a call for more “weirdos and misfits” in government. But the research clearly shows we hire people who most remind us of our most favorite person: ourselves. Firms like IBM are trying to use artificial intelligence (AI) and machine learning (ML) to improve cognitive diversity in hiring by eliminating unconscious biases.
Even in areas like addressing the gender pay gap, while some simple tweaks like making public the median wage of women and men in each department of a company can make a difference, this is another area where there are interesting experiments underway to use AI and predictive analytics.
What is particularly exciting is that some countries like Singapore are leading the way in using technology in using data and evidence based-decision tools to reinvent government HR practices in recruiting, prioritizing new skills, training, and motivation and mental health.