Application of Statistics in Human Resource Management

Introduction

HRM departments, since inception have been a hub for employee data as information has to be captured from the entry to exit for all the employees in organizations. Setting up of HRIS was an advancement over maintenance of a simple database. Unfortunately, the rich base of information was not put to good use. HR professionals were not aware of applications or tools to exploit this data for informed decision-making. Historically, HR decision were based more on personal judgment, intuition and experience and hence were very subjective in nature.

Although there were significant innovations in the realm of HRM in terms of the processes and practices over the last few decades, HRM could not establish its credibility or gain recognition for its contribution to the organizational value chain. HR witnessed a significant shift in its responsibilities from “doable” to “deliverable” (Ulrich, 2013), hence the focus not on outcomes rather than actions. HR initially grappled with its role as strategic partner as HR needed analytic and databased decision making competencies to mesh HR practices and processes with business strategy. For a fairly long time it lacked the right metrics and analytical models (Lawler, et al., 2004). HRM investments were seen as wasteful and it was imperative for HR to enhance efficiency in its systems and derive metrics for their optimum functioning. The targeted areas initially were refining workforce planning and recruitment, reducing recruitment and retention costs and retaining critical talent (Narula, 2015).

This led to the emergence of HR metrics, dashboards and the HR Scorecard (Becker, Ulrich and Huselid, 2001). Organizations utilized different arrays of metrics to assess the effect of HR initiatives in terms of efficiency, effectiveness and impact on business strategy and business performance (Boudreau ad Ramstad, 2005). These metrics reflected current scenarios but failed to provide pointers or projections needed for strategic alignment of HR.

Earlier numerical analysis entailed usage of basic visualization tools such as bars and charts to depict trends and patterns of HR data. The emergence of quantification of decisions in different fields of management called for rigorous use of numerical data (Pfeffer & Sutton, 2006 and King, 2016). Researchers emphasized upon the need for HRM to become more evidence based with application of appropriate numerical rigour in their work (Lawler, 2007). It was felt that metrics by themselves were not adequate and there was a need for application of sophisticated statistical tools but in most cases the process of utilization of numerical data had been elementary (Mondore, Douthitt and Carson, 2011; Lawler, Levenson and Boudreau, 2004). This led to the re-emergence of analytics in the field of HRM. The concept of analytics was not new to HR, the very basic application began with statistical analysis of training investments (Fitz-Enz, 2010). Fitz-enz in 1978 had advocated the use of analytics proposing that HR initiatives and their linkages with the bottom-line should be explored but this new idea did not find any takers at that time (Handa and Garima- 2014). During present times, application of quantitative techniques in HR has been triggered by the datafication of HR (Bersin, 2013), evidence based HR (Lawler, 2007) and use of big data in management (George, Haas and Pentland, 2014).