HR Analytics driving business outcomes: A descriptive and predictive Case of Analysis

Abstract

As the importance of digital technologies in Human Resources, which have always been at the very core of the organization’s success, has increased, so has research on tools, processes, and theories for doing this more effectively. In particular, this master thesis will examine the use of data analytics in the field of human resource development, though its rise in popularity is accompanied by skepticism about the ability of HR professionals to effectively utilize data analytics to reap organizational benefits and to align HR function to the core business goals. Hence, overall, the main focus of this work will be on how the transformation of the HR function, through analytics endeavors, is leading operational HR processes to become data-driven and result-oriented, changing managers’ approaches to evaluate their employees. More precisely, in the first chapter, it will be provided a review of the main issues around the debate on HR analytics, both supportive and critical to this discipline. Initially, it will be explored the concept of HR Analytics in general, along with the forces that have driven its rise and the reasons why is so urgent and beneficial to be promptly embraced by organizations; then it will also explain some barriers to implementing it and limits like the worrying ethical debate generated around it. In the second chapter, it will be investigated the traditional overall scenario, procedures and protocols of how HR treats, uses and interprets available employee data to learn, identify patterns and make decisions, precisely explaining how this has changed over the last decades thanks to new complex free-decision mechanisms techniques used to read and analyze worker data. Accordingly, it will be stated that many companies do not take advantage of the availability of more sophisticated computational methods: HR departments should be much more evidence-based and much less Tayloristic as, nowadays, simple employee workloads are not the dimensions HR is most interested to analyze to gain insights of employee effectiveness and efficiency. In the third chapter, it will be illustrated how quantitative descriptive and predictive techniques may positively influence the management and development of human resources, proving some cases on the field. Regarding the descriptive part of analyses, intuitive HR dashboards will be developed along with some clustering techniques, while, in the more advanced predictive analyses, with the use of the programming language of Python, it will directly analyzed a dataset, realistically simulated, through intelligent machine-learning algorithms on the front-line areas of interest of HR: employee engagement, turnover risk, performance Appraisal and son on. Overall, the analyses will suggest how this type of data-driven approach, which helps identify factors deeply affecting employees behavior, facilitates the creation of a sustained and high-performance ecosystem within an organization, thus increasing the productivity of the employees and in turn increasing revenue generation. Eventually, the same last chapter will regard the interpretation and the limits of this study and will explore the practical implementation of interventions of HR departments, aimed at adjusting their initiatives according to the analyses findings, and a reflection on their possible repercussions on the general HR theory.