Human Resource Analytics and Data-Driven Decision Making: Implications for Talent Acquisition and Retention Strategies

Abstract

The increasing availability of workforce data and advanced analytical tools has transformed the role of human resource management from a primarily administrative function into a strategic, evidence-based discipline. This study investigates the influence of human resource analytics (HRA) on data-driven decision making (DDDM) and examines its implications for talent acquisition and talent retention strategies. Employing a quantitative research design, data were collected through a structured questionnaire from 296 HR managers and senior decision makers across medium and large organizations. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that human resource analytics has a significant positive effect on data-driven decision making, talent acquisition, and talent retention. Furthermore, data-driven decision making significantly enhances both talent acquisition effectiveness and talent retention outcomes and partially mediates the relationships between human resource analytics and the two talent management outcomes. These findings provide empirical evidence that analytics-driven HR practices improve recruitment efficiency, quality of hire, and employee retention by enabling more accurate and proactive HR decisions. The study contributes to the growing literature on HR analytics by clarifying the mechanisms through which analytics creates value in talent management and offers practical insights for organizations seeking to leverage data-driven approaches to achieve sustainable human capital advantages.