Employee Efficiency with HR Analytics Maturity

Human resource management is going through a massive change due to an unexpected change in the working paradigms. Almost all the IT companies have created HR policies enabling moving from co-working to work from home (WFH) options for their employees, creating its advantages and limitations. Optimising the performance and cost and ensuring safety is the paramount need for most of these organizations.

There is enough evidence that HR analytics can bring substantial benefits to the organization, from recruitment to customer engagement to performance improvement [1]. However, the research also indicates that only about 4% of the organizations are implementing HR analytics with success. Another similar research as shown in the below diagram shows that about 86% of the organizations are implementing/planning to implement HR analytics in the coming years [2]. The question remains what, why and how, to implement the HR analytics in an enterprise at all sizes; small, medium or large, is still open for discussion.

In implementing any improvement, it is necessary to understand, what, why and how, of the activity that is being implemented. Any set of information/protocol, which explains both why, how, and what is called a framework.

HR analytics framework looks like an art than a science at this point. There are multiple frameworks for adopting analytics for HR functions ranging from stages to 6 stages and beyond. Also, there are concerns on should the progression of HR analytics adoption be linear and gradual or contextual. In this post, we will discuss some of the common frameworks and provide pointers in adopting our proprietary framework called ‘Employee Life Cycle Analytics (ELCA) developed by the researchers and practitioners at RACE labs at REVA University.

Progressive or Perspective

The number of stages of maturity of the HR function to adopt analytics is given by multiple groups/researches. All of them have merits and limitations. Some of these frameworks are created by professional organizations, some by consulting companies and some from software vendors. First of these frameworks is provided by a practitioner [3]. It suggests a two-stage progression from current practices to best practices in HR analytics. It marks where the adoption of analytics in HR function hits the wall and further progress plateaus.

The next framework comes from a professional body, which suggests a three-stage approach to the adoption of HR analytics [4]. This can be thought of an extension to the previous two-stage framework, but more directional in adopting analytics in HR. Instead of just having basic/advanced analytics, this framework suggests, how organizations are categorized based on the current practices of HR analytics. They can move from an aspirational company to a transformational enterprise in managing people.

This is followed by a four-level maturity model, adopted from the likes of CMM from Carnegie Melon University [5]. Multiple experts have contributed to giving their perspectives to this maturity continuum. One of the best known is from Josh Bersin from Deloitte [6]. It progresses from being an adhoc, to proactive, to strategic and predictive forms of analytics as to the possible levels of progression for an HR function.

Next in the line is a five-stage maturity model for adopting analytics in HR, from Oracle [7]. Oracle being the preferred database solution provider for many organizations, has proposed a technical solution, for which it has a product at every stage. Our take on this model is the cost and complexity of implementing a solution like this, is difficult for most of the organizations.

The number of stages goes on increasing, but the value that can be derived from each stage becomes difficult to quantify and the overlapping of the functions across different maturity levels makes it more complex to impellent. One such framework is given by Fosway group [7]. The frame becomes complex at one end of the spectrum by making it more automated, removing the need for HR analyst, with domain knowledge. This looks more like a technology solution than a business solution.

RACE model for HR Analytics Maturity

Looking at adopting neural networks for fraud detection by credit card companies in the early 90s [8], gives a clue to the adoption of analytics for solving business pain points. Similarly, when the frameworks are based on theory or technology, they fail to reach the masses in a particular industry/function. At RACE lab, after doing an exhaustive study of the currently available frameworks for adoption of analytics in HR, we found that the HR fraternity is not averse to the adoption of analytics, but confused about what and how. Researchers at RACE Lab, have designed a ground-up framework based on the two-stage model of adopting analytics combined with the functional points in HR management. This has resulted in a surprisingly simple framework, similar to the early adoption of analytics by financial institutions – solve a business problem.

The beauty of this model is, it is both specific and generic at the same time. It provides pre-defined models at the same time allows flexibility to adapt to suit the customized needs of an organization in HR analytics. Multiple case studies from the field provide sufficient evidence for us to propose this model for HR groups.

To know more about this research, do write to racelabs@reva.edu.in.

For consulting and adapting this model to your organization, do check here (Link to Consulting – ELTV)

Tags: Whitepaper; HR Analytics; Predictive Models; Dashboards; Employee Efficiency


[1] Workforce Analytics: How mature are organizations?, https://www.analyticsinhr.com/blog/hr-analytics-case-studies/, last accessed: 22 Apr 2020

[2] https://www.peoplematters.in/article/hr-analytics/workforce-analytics-how-mature-are-organizations-13135, last accessed: 22 Apr 2020

[3] HR Analytics: Breaking through the wall in HR measurement,  https://johnpoppelaars.blogspot.com/2015/08/hr-analytics-breaking-through-wall-in.html, last accessed: 22 Apr 2020

[5] https://cmmiinstitute.com/learning/appraisals/levels , last accessed: 22 Apr 2020

[6]  https://joshbersin.com/?s=maturity, last accessed: 22 Apr 2020

[7] Learning and Talent Analytics, https://www.slideshare.net/NetDimensions/fosway-grouplearning-talent-analytics, Last accessed 22 Apr 2020

[8] Evolution of fraud analytics,  https://www.kdnuggets.com/2014/03/evolution-fraud-analytics-inside-story.html, Last accessed 22 Apr 2020

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