HR Analytics: Leveraging Big Data to Drive Strategic Decision-Making in Human Resource Management

Abstract

The research paper examines how HR Analytics can be used to leverage big data in making decisions in Human Resource Management (HRM) that lead to a transformation. By collecting, analyzing and interpreting vast amounts of employee data systematically, the HR analytics offers actionable insights for workforce management optimization, productivity enhancement and alignment of human capital with organizational objectives. The study incorporates major theories such as resource-based view and human capital theory, and employs advanced analytical models like regression analysis, logistic regression for turnover prediction and k-means clustering for workforce segmentation. Results show that engagement and training significantly affected performance while effective prediction of turnover was based on metrics of performance and engagement. Clustering shows separate employee groups that promote directed HR strategies. This research highlights the need for data driven HR practices to gain competitive advantage and achieve organizational success. It also emphasizes ethical considerations as well as calls for further research incorporating advanced machine learning techniques, real-time data analytics, and long-term effects of HR Analytics. As a result, this paper presents a solid structure enabling HR practitioners to implement data-driven approaches that enhance an environment of continuous improvement in HRM.