HR Analytics: Trends, Challenges and Implementation for Organizational Effectiveness

Abstract

 In spite of the fact that Human Resource Analytics (HRA) has existed for some time, its popularity has recently increased due to the growing need for data-driven decision-making in organizations. HR Analytics, also known as workforce analytics or people analytics, enables organizations to measure, analyze and enhance HR practices for improved business performance. This study investigates the processes, antecedents and barriers in adopting HR analytics, highlighting models such as Logistic Regression, Random Forest, Gradient Boosting, Decision Tree and K-Nearest Neighbors. Balanced strategies such as SMOTE, ADASYN and random oversampling are assessed to address data imbalance issues. Using data from an aluminum company, the study explores attrition-related patterns and provides insights on job mismatches, tenure-based attrition and merit score impacts. Findings suggest that effective application of HR analytics not only improves retention strategies but also enhances organizational competitiveness. The paper concludes with practical recommendations for overcoming current barriers and leveraging HR analytics for sustainable growth.