Industry Research Summary: The State of HR Analytics 2021

Increase HR’s effectiveness by mastering people analytics

HR analytics* has evolved from a nice-to-have to a must-have for most organizations. However, little about people analytics is easy. It can be hard to measure, collect, clean, and draw insights from data. It’s even harder when there’s a need to integrate data among multiple systems. So how is HR doing with initiatives to create useful analytics? HR.com’s HR Research Institute conducted an exclusive study that included a wide range of organizations, some of which are doing well with HR analytics and many of which are struggling.

Key Findings

• Some organizations have become good at people analytics, but most are not there yet.
• Some significant differences are revealed between organizations that are not very good at HR analytics and those that excel in this area.
• HR analytics provides useful insights to HR leaders more than to other stakeholders.
• Organizations mostly rely on descriptive HR analytics rather than on the more sophisticated predictive and prescriptive analytics.
• The most difficult elements of people analytics are data integration, cleaning and visualization.
• HR professionals use a variety of methods for distributing and presenting HR analytics.

HR Still Needs to Up Its People Analytics Game

Only 29% of HR professionals say their organizations are good or very good at making positive changes based on people analytics. Fewer than half of organizations (44%) have made it to the first rung of the analytics ladder by being good or very good at gathering people analytics. The numbers drop off steadily as they move up the ladder. Just over a third (36%) are good or very good at evaluating analytics. Slightly fewer (33%) are good or very good at communicating the results, and fewer still (29%) are good or very good at making positive changes. Many organizations (39%) still can’t get much traction with people analytics. They report that their organizations are poor or very poor at designing and implementing processes to get value out of people analytics. Those organizations may want to hire professionals from companies of a similar size that have been successful with analytics. As for those that are moderately good in this area, it might just be a question of patiently improving year after year, carefully building credibility so that it is possible to gather more expertise and resources to devote to HR analytics.

Nearly two-fifths (38%) of respondents say people analytics is very effective at providing important insights to HR leaders. An additional 40% say they are somewhat effective at this. Since HR leadership is the primary target of HR analytics, it is reasonable that this is the group that gets the best service.

We asked HR professionals about the top HR functions in which people analytics are most important. The most commonly cited answer was compensation. Compensation professionals are typically comfortable with analytics, having crunched numbers for decades. However, as organizations focus more on issues such as pay equity and performance management, even compensation experts are expected to conduct more robust and insightful analyses with the data at hand. The second most commonly cited HR function is recruitment and selection, while the third most common is in the area of employee engagement.

Some Aspects of People Analytics Are More Difficult, Especially Data Integration

The two most difficult areas of HR/people analytics are data integration and data clean up. Nearly half of HR professionals (49%) find data integration fairly or very difficult and almost as many (42%) find data clean up fairly or very difficult. Encouragingly, only 20% find data security fairly or very difficult, presumably because the organization has invested the needed resources in cybersecurity.

HR Leaders Are Often Actively Involved with Analytics

It is encouraging to see that more than half of respondents (53%) agree or strongly agree that the head of HR is actively involved in directing people analytics. It is also somewhat encouraging that nearly half (46%) agree or strongly agree that business managers are highly supportive of people analytics.

Analytics Technologies Are a Blend of Old and New

Most organizations (56%) still use spreadsheets to perform HR analytics. This should be no surprise; spreadsheets are cheap, familiar, and ubiquitous. What is of more interest is what other analytics tools HR professionals are using. Forty-one percent are using the analytics tools built into their core HCMS/HRIS system or their other HR technologies (e.g., LMS, ATS). These built-in tools are gradually getting better as vendors come to understand the analytics needs of their users.

Most organizations use more than one method to deliver people analytics reports to leaders. About half of responding companies use dashboards as one of these methods. Even more popular is email, which is used by 68%. Interestingly, even in today’s digital age, 41% still use paper reports as one way to deliver people analytics to senior managers.

The term “big data” typically refers to data that is so voluminous, fast-flowing, and complicated that it is difficult to process using traditional statistical methods. So, is HR data a type of big data? We discovered that only a minority of HR professionals believe that their HR analytics data reflect these characteristics to a high or very high degree. Of those five traits, HR data is most likely to be seen as valuable (33%) and valid (27%). But the sheer volume of HR data is modest compared to other sources of information, such as that gathered in retail data centers. Nor does the data flow and change as rapidly as some other areas such as stock market data.
   
*For the purpose of this study, we describe HR analytics as follows: Also known as people or talent analytics, HR analytics refers to the process of collecting and analyzing people-related data for the purpose of producing insights and improving decision-making. HR analytics generally includes the use of statistical methodologies, technologies, and expertise. It is sometimes coupled with the term metrics, and it may include the analysis of both “big data” and smaller data sets.
 

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