Speaker's blog: Moving from Descriptive to Predictive HR Analytics

Written by: Luk Smeyers CEO and Co-Founder

We regularly meet organizations that have very impressive HRIS implementations with great dashboards and reporting capabilities. However, almost all of them struggle to understand how Predictive HR Analytics could augment their existing HRIS capabilities. The purpose of this short blog is to clarify the main differences between HRIS (a.k.a. Descriptive Analytics) and Predictive HR Analytics (a subset of data science) and highlight their complementary natures. In particular, you will learn that Predictive HR Analytics:

  • can provide more insightful and actionable answers to the organization’s most common questions (“Who are my most valuable employees?” “Which employees are most likely to leave, sell, perform, get promoted, collaborate, or drive customer satisfaction?”) than those generated by HRIS alone.
  • can provide more future-looking answers and recommendations to questions that cannot be addressed at all by HRIS.
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1. HRIS (Descriptive Analytics) versus Predictive HR Analytics

Here's a pretty common way to define the worlds of HRIS and predictive HR analytics:

  • HRIS is the world of Descriptive Analytics: retrospective analysis that provides a rear view-mirror view on the business—reporting on what happened and what is currently happening.
  • Predictive HR Analytics is forward-looking analysis: providing future-looking insights on the business—predicting what is likely to happen (usually associated with a probability) and why it’s likely to happen

HRIS looks for trends at the macro or aggregated levels of the business, and then drills up, down or across the data. Areas may include: geography, time, employees, departments, business units stores, performance, talent potential or other business dimensions. HRIS is about descriptive analytics (or looking at what happened), slicing and dicing across the HR data.

Predictive HR Analytics, on the other hand, builds analytic models at the lowest levels of the business—at the individual employee level—and looks for predictable behaviors, propensities, and business rules (as can be expressed by an analytical or mathematical formula) that can be used to predict the future likelihood of certain behaviors and actions. Predictive HR Analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes.

2. Moving from HRIS to Predictive HR Analytics

To be able to move from HRIS to Predictive HR Analytics, a deep understanding of the differences is essential. Maybe the easiest way to understand these differences is to look at the answers they can generate.

For example, HRIS allows you to answer questions about the demographics, characteristics or costs of your employees and answers questions about the performance of your employees across a number of different dimensions. On the other hand, Predictive HR analytics allow organizations to go beyond the answers generated by HRIS by providing more predictive answers and recommendations for many of the same questions.

Use HRIS to gain descriptive insights about employees and then use Predictive HR Analytics to build predictive models and actionable recommendations at the individual employee level.

Predictive HR analytics take the questions that HRIS answers to the next level, moving from a retrospective set of answers to a set of answers focused on predicting performance and prescribing specific actions or recommendations.

3. Conclusion

No matter what, you still need HRIS to know what really happened in the past, but you also need Predictive HR Analytics to optimize your resources as you look to make decisions and take actions for the future. The biggest challenge for HR professionals however is to build the capability and expertise to move from a descriptive, retrospective approach (as in most HR departments) to a future-looking, predictable approach. By describing the core differences, we hope that HR can start making progress in the critical analytical space with the objective to improve their decision making quality.

This is a summary of Luk's original blog post, which includes great examples of descriptive and predictive questions, overviews and figures.