Using HR analytics to support managerial decisions: A case study

Abstract

Human Resource (HR) Analytics enables HRs to make strategic contributions and support managerial decisions. However, in most of the industry, HRs should have been on board with data analysis. There are several challenges: the HR data is messy and imbalanced, it is hard to harness both structured and unstructured data, some HR managers lack data mining skills and the lack of related empirical research that gives a detailed analytics guideline. The contribution of this study is that we develop a framework to support an industrial aluminum company to make the decisions and to improve strategy execution. The framework includes descriptive analysis, predictive analysis, and entity sentiment analysis. We analyzed an industrial aluminum company's data and found some actionable issues. Then we employed machine learning algorithms to predict employees' turnover and found risk factors. Moreover, we applied the entity sentiment analysis on the unstructured data collected from employees' engagement survey.