HR big data comes with a big reputation: it can transform business decision-making, and it can boost credibility and influence. In fact, a lack of data-backed decision making is damaging HR credibility, as shown by this report by KPMG, which revealed that 85 percent of CEOs surveyed believe that HR teams ‘fail to provide insightful analytics’ and that this erodes trust.
Many HR professionals who don’t use big data may be wondering what all the fuss is about and may be skeptical as to whether it really can make a difference. This is why I thought it would be useful to provide a rundown of four HR questions that big data may be able to answer better than a human can.
1. What Is The Ideal Person Specification For a Role?
This question is usually answered based on perception and hunch, yet we find that there is a much better data-centered way to answer this question. Just look at Xerox, which developed a ‘people model’ for the ideal call-center worker based on a big data analysis of its workforce. It found that the ideal person lives near the job, has reliable transportation, and uses at least one social network, but not more than four, oddly. They also aren’t “generally inquisitive” or “empathetic,” but they are “creative.” Xerox used a computerized assessment process to screen and select candidates against this strict criteria. In the first half-year of trialling this decision, Xerox cut attrition rates by a half. This showed the big data decision to be superior to the human decision in this situation.
The next two questions come from Google, the modern masters of HR big-data backed decision-making. Google’s team has been doing it for the last six years, and look where that got them!
2. What Is the Optimal Onboarding Agenda to Maximize New Hire Productivity?
Now, onboarding doesn’t occur in a lot of companies, and when it does occur, its design is often based on hunch and perception and accepted wisdom. However, Google took a data-backed approach to this, and after assessing performance data of new hires, it created an optimal onboarding agenda which boosted productivity by 15 percent.
3. What Is the Optimal Team Size and Shape?
In most companies, this is total guess work. Teams grow and develop, randomly, organically, and without any particular strategic design. How could you possibly know what the ultimate team size is anyway? And if you did have a hunch that teams were not well designed or too big or small, you aren’t likely to be listened to unless you do what Google did and use big-data analysis to reveal the optimal organizational size and shape of teams.
4. Do Grades, University and References Matter in New Applicants?
Most of us assume that the grades and the amount of experience a person has before starting a job will be proportional to how well they perform in the job. Why question one of the oldest and most accepted wisdoms of hiring? Well, a multi-billion dollar insurance company challenged this assumption via big-data analysis.
Yes, the company had always assumed that top sales people must have top degrees and grades from top schools and should have experience selling high-value products. However, the data showed this belief system was wrong and that these qualities did not matter. What did matter was: no errors/typos on resume, finishing school, experience selling cars or real estate, success in previous jobs, the ability to deal with vague instructions, and good time management skills. Within six months of adapting the assessment processes to reflect these findings, revenues increased by $4 million. Once again, the data-backed decision was superior to the human decision.
These questions are just a start, and there are many more questions that big data can answer for HR. Big data should not be left to eggheads and Ivy League data scientists. Big data has a very grounded role to play within the HR function: giving enhanced insight to every day HR situations.