Feature
Who thought data in HR could get more complicated?
Not long ago, analytics was a support tool for HR, used to track retention or concoct surveys to track employee engagement. But as technology has become more sophisticated, employers are using people analytics more strategically. That puts pressure on HR leaders to look on data less as a nice-to-have and more as a requirement for effective workforce planning and development.
That involves two kinds of analytics, “predictive” and “prescriptive.” In short, predictive analytics forecasts what may happen, while prescriptive analytics suggests action to take in anticipation of it happening. Only about 20% of employers use predictive analytics in HR, while just 17% regularly use prescriptive analytics, according to HR.com.
Given the pace at which the business landscape and job requirements evolve, it’s surprising adoption isn’t higher. Identifying and understanding trends in the workforce is important. Skills requirements change, remote work is not as outlandish an idea as it once was and employees increasingly expect personalized help from companies to develop their careers. Reviewing past trends or relying on gut feel won't work in this context.
HR technology vendors have positioned predictive and advanced analytics as core differentiators as a result. SAP, for example, pushes its capability to link data from its Business Data Cloud to solutions in SuccessFactors. That, the company claims, helps HR make more informed decisions about hiring needs, compensation and skills. Workday delivers workforce intelligence including growth opportunities and risks.
Vendors and their customers are in sync on this. The market for HR analytics will be worth $9.9 billion by 2031, up from $3.7 billion in 2024, according to research from Insight Partners.
HR’s Digital Crystal Ball
Businesses have always wanted better data to help them understand their past performance and plan for their futures. As technology has become more sophisticated, it’s also become more connected. Accessing real-time and integrated data on the workforce is easier. Vendors have reduced data silos, so recruiting data is often stored on the same system as data on learning and data on performance. Even beyond data-sharing, developers are emphasizing integration, allowing products from different vendors to share information.
Still, the focus of these data products tends to be backward-looking. They help HR understand past trends. So, for instance, HR might use analytics to determine how effective their learning programs are, or where retention may be at risk.
More often, HR is using analytics to foresee challenges and plan strategies for meeting them. HR departments are increasingly expected to forecast which employees might leave, identify the highest-potential candidates or anticipate skills gaps. HR is expected to anticipate a company’s workforce needs, not simply report what’s happened.
HR Data in the Real World
Doing this isn’t easy. Even with the newest technology, employers face challenges with data quality and silos. If those aren’t addressed, analytics aren’t as helpful. And, amidst all the talk of corporate-wide skills gaps, HR faces a skills gap itself: Many teams can’t interpret models and take appropriate action.
In addition, there’s trust. If the data and analytics behind hiring or promotions are seen as unfair or less-than-transparent, employees view HR’s work skeptically, which undermines data’s usefulness.
For example, a company may experience a high attrition rate in certain departments. By bringing together data from onboarding, performance reviews, engagement surveys, learning and exit interviews, HR identifies specific teams with high turnover, which departments are less likely to pursue internal mobility or what the time-to-productivity looks like for each recruiting channel.
Once that’s done, the company uses predictive analytics to uncover which employees are at risk of jumping ship. Perhaps when onboarding is shorter, engagement drops earlier, followed by a dip in performance. If a system alerts HR and line managers, they explore ways to improve employee development or reduce the attrition risk on an employee-by-employee basis.
Or, HR dives into the link between onboarding and business results. Does shorter onboarding lead to an increase in project delays? Does it improve or damage retention? How do onboarding periods lead to higher or lower recruiting costs?
Sophisticated Use of Sophisticated HR Data
All of this requires HR to become more sophisticated in its use of analytics. And while understanding analytics may come close to being rocket science, developing a more sophisticated approach isn’t. Identifying specific examples helps prioritize efforts, and making sure analytics capabilities align with business priorities helps concentrate resources in the right places. And, of course, HR must be trained on how to interpret data and how to act on the intelligence it produces.
There’s no question businesses’ use of analytics is already becoming more sophisticated. AI copilots today help users of all sorts sift through and understand data, then recommend actions to take. Internal talent marketplaces match skills with current opportunities. The idea of skills intelligence is gaining traction. All of this demonstrates HR’s opportunity to use data analytics to support its transition to a strategic operation.
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Analytics helps HR move from managing people to creating workforce intelligence, using data to anticipate change and deliver measurable business results. The greatest value of analytics lies not in creating more dashboards, but in helping HR make decisions as its importance to the company rises.
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