There are few areas of human resources that seem to be more tailor-made for data analytics than compensation. Not only is employee pay by far the largest expense for most companies, but pay decisions themselves would benefit from more analytical rigor in many cases. The question is, why don’t more companies use data analytics when it comes to compensation?

Companies that crack the code on this question could get a leg up on competitors. The value of analytics to the compensation function lies in using this insight to make better and more fact-based decisions about compensation and to make sure those pay decisions have the intended effect on employees and the business as a whole.

“It is very common for compensation professionals to rely on very basic data, such as internal and external benchmarks,” says Wendy Hirsch, a principal with Mercer’s workforce strategies group in Milwaukee. “Rarely do they leverage more advanced analyses that allow them to think more strategically about the impact of compensation decisions.”

Indeed, a 2012 study conducted by Mercer and World at Work found 90% or more of organizations use benchmarking among internal and external peer groups and 87% rely on ongoing reporting to make pay decisions. While Hirsch notes that there is tremendous value in using internal and external benchmarks to ensure that the company’s pay is well positioned relative to the market, there is also a place for more in-depth analysis. However, more sophisticated analytical techniques such as projections (80%), simulations (64%) and, especially, predictive modeling (43%) are utilized much less often than more traditional compensation tools.

Companies that do leverage this more analytical and advanced thinking could uncover insights on everything from how compensation drives workforce retention and performance to the role of compensation in overall business performance. For CFOs, using data analytics in this way is likely to seem like a win-win for all involved.

However, before CFOs move in that direction, they need to understand what is going on with their colleagues working in compensation. Hirsch notes that compensation professionals feel that they have the skill set and the capabilities to use data analytics in doing their jobs. However, they are less confident in the data available to analyze. For example, in companies using a total rewards approach, compensation professionals tend to be concerned that data related to certain aspects of the total rewards equation, particularly education, competencies and investments in training, is not strong or readily available enough to support data analytics.

In addition, CFOs need to tread carefully. While having finance lend its expertise and analytical capabilities can certainly lead any data analytics effort to a stronger and more credible result, it is important to remember that HR and compensation professionals bring their own unique expertise to the table. In upcoming posts, we will talk in more depth about the role of the CFO and finance staff in compensation data analytics, as well as what types of insight companies can gain from leveraging data analytics to make compensation decisions.