Data-Driven Decision Making: A Framework for Integrating Workforce Analytics and Predictive HR Metrics in Digitalized Environments

Abstract

This research offers a methodology for combining predictive HR indicators and workforce analytics to support data-driven HRM decision-making in digitalized settings. The study investigated the difficulties, prospects, tactics for executing, and optimal approaches related to the amalgamation of workforce analytics and predictive HR metrics. Additionally, the study sought to ascertain the policy ramifications for both firms and legislators. The study thoroughly analyzed prior research and secondary data sources to investigate the topic. The significance of data quality and governance, organizational alignment and leadership support, cooperation and cross-functional engagement, training and development, piloting and iterative improvement, and ongoing learning and adaptation are among the key conclusions. To facilitate the adoption and optimization of data-driven decision-making in HRM, policy implications include the requirement for data governance frameworks, training and development programs, regulatory frameworks, and incentives for innovation. This framework offers insightful analysis and helpful recommendations for firms using data to improve workforce management procedures and foster organizational performance in digitalized settings.