HR Analytics: Moving from Descriptive to Predictive Analysis
We regularly meet organisations 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, 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.
1. HRIS (Descriptive Analytics) versus Predictive HR Analytics
Figure 1 (click on the image to enlarge) is a pretty common way to view the worlds of HRIS and predictive HR analytics:
- HRIS is the world of descriptive analytics: retrospective analysis that provides a rearview 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.
Figure 1: HRIS versus Predictive HR Analytics (click on image to enlarge)
HRIS looks for trends at the macro or aggregated levels of the business, and then drills up, down, or across the data to identify areas of under- and over-performance. 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 dimensional models with massive dissemination to all business users.
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 behaviours, propensities, and business rules (as can be expressed by an analytic or mathematical formula) that can be used to predict the future likelihood of certain behaviours 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 to 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’s level as displayed in Tabel 1 (click on the image to enlarge):
Table 1: Different answers from descriptive or predictive HR analytics (click on image to enlarge)
Predictive HR analytics take the questions that HRIS is answering 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. For example, if we change the key descriptive questions in Table 2 (click on the image to enlarge) to a future tense, then you can see that we need a predictive HR analytics approach that is completely different from the conventional HRIS approach.
Table 2: Moving from retrospective to forward looking (click on image to enlarge)
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.
4. About the authors
Bill Schmarzo is known as the “Dean of Big Data”. After being deeply immersed in the world of big data for over 20 years, he shows no signs of coming up for air. Bill speaks frequently on the use of big data, with an engaging style that has gained him many accolades. He has written several white papers and articles about the application of big data and advanced analytics to drive an organization’s key business initiatives. Prior to joining Consulting as part of EMC Global Services, Bill co-authored with Ralph Kimball a series of articles on analytic applications, and was on the faculty of TDWI teaching a course on designing analytic applications. Bill is the author of “Big Data: Understanding How Data Powers Big Business” published by Wiley.
Luk Smeyers is an experienced senior HR executive who has lead complex transition projects for compelling Fortune 500 companies, such as PepsiCo, Starbucks and Nielsen. In 2008, Luk started a cutting-edge predictive HR Analytics consultancy, together with academic partner Dr. Jeroen Delmotte. Luk is widely recognized as one of the few European top HR analytics experts. He is revered as a leading thinker, educator, influencer and is a well-known content contributor, blogger, columnist and author of many articles. He is an invited speaker at international conferences and helps clients set a higher ambition for strategic HR intelligence, leading consultative projects in the Benelux countries with such organizations as ING, KPN, ABN-AMRO, Philips, Rabobank, UWV, RealDolmen, Acerta, NS, BASF, Besix, Strukton, Bekaert, Randstad, Eandis, AG, Postnl, AON, Raet, etc.
Interested in using predictive HR analytics as a key component in your HR strategy? Get in touch with CEO and co-founder Luk Smeyers for more information (email@example.com or via Google+). Or follow iNostix on Twitter and/or Facebook for exciting international articles on HR analytics.