Introduction
Human Resource (HR) Analytics is an essential tool for HR professionals as it enables them to create better working environments and maximizing employee productivity, thereby having a significant impact on an organization’s bottom-line. However, the primary issue facing HR Analytics is that it relies on data sources, which are weak measures of return on investments, and this consequentially limits the strategic influence of the HR and lowers employee well-being. Recently, the amount of data stored and manipulated by HR has increased such that there has been a need to inculcate big data analytics into its processes.
For instance, the advancement of the human resource information system (HRIS) has made it possible to conglomerate data that was initially stored separately (Angrave, Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016). However, although the inception of big data analytics is beneficial as it enables the collection of large volumes of employee data, its use still poses a significant ethical risk. Overall, the integration of big data technology in the HR industry is still developing; therefore, this suggests that the relatively high potential of big data HR analytics has not been fully exploited. HR analytics is centered on the following four principles: creation, capturing, leveraging, and upholding the value offered by human capital.
The limitation of this article is that it emphasizes on what should be done rather than how it should be done (Angrave et al., 2016). Therefore, this further mirrors the fact that there are still numerous barriers impeding the successful adoption of HR analytics.
Barriers to Successful HR Analytics Adoption
The primary barrier affecting successful HR analytics adoption is the weakness in the HR profession and analytics industry itself. Concerning the profession, the barrier is mirrored by their silo mentalities that limit their manipulation of human capital as metrics. Conversely, in the analytics industry, the problem lies with the manner through which the analytics products are being promoted and sold. For instance, the process through which HRIS is procured, which is the benchmarking of new HRIS systems installed against that of older HRIS systems, is both time and cost consuming. Furthermore, the HRIS is often inclined towards using the “best practice” approach, which limits its efficacy and capabilities.
Other problems with HR analytics are centered on the changing corporate environment, from “talent to people”, and the development of products that fail to tackle the new environmental challenges. Overall, these before-mentioned barriers have resulted in firms being reluctant to invest in developing their HR analytics capabilities.
What is to be Done
One provocative way through which the limitations of the adoption can be mitigated is through an outside-in process. However, the “outside-in” approach is impractical as different firms have different environments, which ultimately affects the efficiency of the process. This is further mirrored in the empirical literature, which evidences a correlation between modelling and algorithm-based approaches and reduced job quality and performance (Angrave et al., 2016). Moreover, HR professionals should seek to challenge reports generated by proprietary analytical software to get an optimal solution that is most applicable to their organization.
However, although controversial, the best alternative is through academics. On the one hand, through the inclusion of academic researchers, firms can develop more advanced forms of longitudinal, multivariate econometric models required to perform end-to-end analytics. However, on the other hand, the efficacy of academics in bridging the gap is dependent on HRIS analytics packages, the ability of academic researchers to elucidate the practice of HR analytics and confront ontological and methodological issues.
Reference
Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11. Web.