Role of Data Analytics in Human Resource Management for Prediction of Attrition Using Job Satisfaction

Abstract

The reputed management publications like Harvard Business Review (HBR) have started stressing upon the emergence of data-driven management decisions. The enhancing investments in data and analytics are underlining the aforementioned emergence. According to International Data Corporation, this investment is expected to grow up to $200 billion by 2020. In such a data lead management world collecting, managing, and analysing the human resources-related data becomes a key for any rather every organization. Human resource analytics is changing into necessary as strategic personnel designing is the need of the hour and helps organizations to investigate each side of HR metrics. HR analytics could be a holist approach. According to KPMG—India’s Annual Compensation Trends Survey 2018–19 the average annual voluntary attrition across sectors is 13.1%. This is a considerably high percentage. Hence, antecedents leading to attrition are needed to be explored in order to propose appropriate HR policies, strategies, and practices. In relevance to these facts, this study focused on proposing a data-driven predictive approach that examines the relationship between the attrition (dependent variable) and other demographic and psychographic independent variables (Antecedents). The present study found that there is a strong relationship between job satisfaction and attrition. Further, there is a higher probability that the employees having work experience between 0–5 years may leave the organizations. Such data-based outcomes may offer help to HR managers in addressing the problems like attrition which intern may increase ROI. Thus, this paper underlines the emergence and relevance of analytics with special reference to human resource management domain.