For decades, HR has lived by the adage, “Go with your gut”—trusting our instincts when it came to hiring decisions, recruiting, selecting perks and benefits, and even completing employee assessments. But today’s technology brings us something far more reliable on which to base the future of your company and the management of its most valuable resource: Big data.
Data-driven HR is here, and the time to adopt it is now. Public companies that use data-driven HR methods, or “people analytics,” show 30 percent higher stock market returns than the Standard & Poor 500 index. Additionally, HR teams who use data-driven methods are four times more likely to be respected by their business associates, according to a Bersin by Deloitte study.
But which data should you use, and how can you employ it to make the best hiring decisions? While you may be accustomed to using historical data—for instance, analyzing exit interviews to find out why talent leaves your firm—big data’s biggest strength is in predictive analysis and future casting. Big data can help you determine where your business is headed if you make a certain decision or choose a particular path.
From recruiting to pinpointing how a new hire will work out, here are just a few ways you can adopt data-driven HR practices.
Talent Analytics for Recruiting and Hiring
Clearly, social media puts more job prospects in our field of vision today than ever before—so many that it’s virtually impossible for any single human being to sift through the vast pool of applicants or potential recruits to find the people with the best chance of becoming long-term hires.
But when we combine big data with deep-learning algorithms, not only can we sift through the data quickly, we can also pinpoint the best candidates with a high degree of accuracy. We can even find those new hires most likely to be top performers and make sure we give them the tools and resources they need to shine, along with the benefits that will make them stay.
Predictive Analytics to Aid in Retention
Good HR directors like to think they can keep a finger on the pulse of their organization. But it’s easy to be blindsided by a good employee who decides to leave if you don’t know the factors that may drive them away. You don’t have to rely on your instincts to spot an unhappy employee.
Instead, use predictive analytics to determine which employees are most at risk for leaving based on a number of key indicators. Then see what you can do to reduce those risk factors, whether it’s reducing their overtime hours or providing stock options.
Evaluate Data to Improve Workforce Planning
Some of our country’s most important jobs, from truck drivers to nurses to tech and security positions, face severe talent shortages. Previously, workforce planning was more a function of budget vs. headcount. However, companies that evaluate only these factors overlook key components. Rather than just balancing the budget to the headcount, use data to predict who is most likely to leave your organization next, when they will leave, and how this will affect your company.
Big data can help you foresee mass retirements, what skills you’ll need to hire for in the future, and how you can keep your workforce stable in a world of ever-changing factors, which includes a shifting economy and the introduction of artificial intelligence (AI) and robotics into the workforce.
Relate HR Data to Business Outcomes
Most importantly, hiring, retention, and workforce planning should all correlate to your business outcomes. Break down departmental silos to analyze data from sales, marketing, and accounting to determine your overall business goals for the next one to three years. Then think about which skill sets you’ll need to add to your organization and what action your department needs to take to help meet these goals.
If you’re sensing you don’t want to be left behind as HR undergoes a data-driven disruption—well, this is one time you can trust your gut. The evidence supports it. The tools are at your disposal. It’s time to gain the competitive edge with data-driven HR.
A version of this was first posted on Converge.xyz
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