The Complexity of HR Analytics Resolved: 5 Perspectives of Definition

I’ve witnessed a lot of interest in the domain of HR analytics this year, among HR and other C-level managers that I met in Tel Aviv. At some point of any networking conversation, I’ve always been asked to explain how different HR analytics is, from the traditional Organizational Research that I’ve conducted for more than two decades now. A comprehensive definition of the “People Analytics” field would be useful to clarify the difference. Unfortunately, I keep finding myself lost in vague and exhausting explanations, since it is still hard to describe the complexity of HR Analytics in one or few clear sentences.

HR Analytics is a growing field. Yet, it is not mature. Many terms in this domain are quite fluid, especially among HR practitioners. I believe that we still lack parsimonious definitions that would lead us through many blurred concepts in this raising professional area. Yet, I keep looking.

A search for definition

Definitions are found all over the web, you may say. Certainly, Google will give you 27 million results in less than a second. The first in my google search would be “Technopedia”, which refers HR analytics to “applying analytic processes to HR department, in the hope of improving employee performance and therefore getting a better ROI.” According to this definition, the objective is to provide insight into business processes by data analysis, and base relevant decisions on data, for improving these processes.

However, this short definition is neglecting many features of HR analytics in practice. For instance, it ignores the massive amount of employee data, which is not directly related to business processes and performance, but may raise important understanding of it. “Bersin by Deloitte” use the term Talent analytics to clarify “the use of measurement and analysis techniques to understand, improve, and optimize the people side of business.” Its definition details many kinds of data, e.g., demographics, job history, training, assessments, and compensation, which “can be correlated and matched to many different types of business data to help companies to understand profiles and behaviors which create high performance”.

Bersin’s definition is indeed broader. Nevertheless, it implies almost nothing about the time point of view. Specifically, do we use data elements for inference (i.e., explaining past or present results), or do we use them for prediction (i.e. learning something about future results)? Although Bersin suggests a framework to measure the analytics maturity, starting low at “operational reporting” and ending high at “predictive analytics”, its core definition actually does not include the time perspective. The importance of time perspective is nicely stated in the book “People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent”, by J.P. Isson and J.S. Harriott. As Isson and Harriet put it, “People Analytics has the most impact on the organization when it is forward-looking - not backward-looking. In other words, it is most useful when it is predictive and provides a lens into the future regarding likely business outcomes”.

5 perspectives of an expanded definition

So far, just by scratching the surface, it is clear that HR Analytics is much beyond the HR department scope, as opposed to old-school organizational researches. It is about the business performance in general, it is involved with different kinds of data, and it is relevant not just for inference but rather for prediction. However, these definitions of HR analytics are not sufficient, in my opinion, to fully distinguish between Organizational Research and HR Analytics.

The complexity of HR analytics domain, let alone the complexity of its parsimonious definitions, is clearly demonstrated in an inspiring article, recently published by our colleague Lyndon Sundmark. Trying to answer the huge question of “how do I get started in the field of HR Analytics”, Sundmark lists the building blocks of HR analytics, in which domain experts must be familiar with. These building blocks include: HR functions and business processes, Information technology and HR information systems, Business statistical analysis, HR measurement and metrics, HR operations, HR decision making and policies, and Data Science framework.

Indeed, a multidisciplinary long-long list. Reading Sundmark’s article, I suddenly realized that the definition which I’m looking for should have some kind of a top-down structure, describing HR analytics through different organizational perspectives. A top-down structured definition may emphasize not only the complexity of this field, but also its vast influence in different aspects of activity in the organization.

Explicitly, I suggest to include five perspectives in any definition of HR Analytics, starting from C-level and business perspective, go through HR processes and through IT and HRIS, and end-up with a Data science perspective and the role of the HR analytics lead:

I believe that such scheme may serve as a diagnostic guideline when approaching a new organization (and not only be of assistance next time I try to explain the differences between HR Analytics and the traditional Organizational Research domain).

Notice that in effect, each level in the suggested structure is influencing and being influenced by the nature of the level on its top. Hence, such guideline may deepen the understanding of the organizational challenges in regards of HR Analytics, and point to the core opportunities for different roles in HR team.

So, how would you define HR analytics? How does my definition structure would facilitate it? I welcome your suggestions in comments.

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Littal Shemer Haim brings Data Science into daily HR activities, to guide organizations to base decision-making about people on data. Her vast experience in applied research, keen usage of statistical modeling, constant exposure to new technologies, and genuine interest in people lives, all led her to focus nowadays on HR Data Strategy, People Analytics, and Organizational Research. This post originally published in Littal's blog on November 2016.