Analytics in Human Resource

For any company to function well, all departments have to utilize certain tools and one important tool for human resource professionals is HR analytics which enables them to create a better working environment and maximize employee productivity. It thereby, has a significant impact on an organization's bottom line. However, the primary issue facing HR analytics is that it relies on data sources that are weak measures of return on investments, which consequently limit the strategic influence of HR and lower employee well-being. 

Lately, it has been discovered that the amount of data stored and manipulated by HR has increased to such an extent that there is a need to inculcate big data analytics into its process. For example, the advancement of the Human Resource Information Systems known as HRIS has made it possible to amalgamate data that was initially stored separately (Angrave, Charland Kinkpatrick, Lawrance & Stuart 2010). 

Although the inception of big data analytics is beneficial as it enables the collection of a large volume of employee data, its uses still pose 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.

A number of barriers impeding the successful adoption of HR analytics have been discovered to be the weakness in the HR profession and analytics industry itself. 

Concerning the profession, the barriers are mirrored by their silo mentalities that limit their manipulation and 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. It is both time and cost consuming. 

Furthermore, HRIS is often inclined toward using the best practice approach which limits its efficiency 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 barriers have resulted in firms being reluctant to invest in developing their HR analytics capabilities.

One proactive way through which the limitations of the adoption can be mitigated is through the outside-in process. However, this process is impractical as different firms have different environments which ultimately affect the efficiency of the process. This is further mirrored in the empirical literature which provides evidence of a correlation between modeling and algorithms approach and reduced job quality and performance(Angrave et al 2016). Moreover, HR professionals should seek to challenge reports generated by the proprietary analytics software to get an optimal solution that is most applicable to their organization.

Although controversial, the best alternative is through academics. On the one hand, through the inclusion of academic research, firms can develop more advanced forms of longitudinal multivariate econometrics models required to perform end-to-end analytics. 

On the other hand, the efficacy of academics in bridging the gap is dependent on HRIS analytics research to elucidate the practice of HR analytics and confront ontological and methodological issues.