Data-Driven Decision Making: A Structure for Combining Predictive HR Metrics and Workforce Analytics in Digitalized Settings

Abstract

In order to facilitate data-driven HRM decision-making in digitalized environments, this study 
provides a technique for integrating workforce analytics with predictive HR indicators. The 
research examined the challenges, opportunities, implementation strategies, and best practices 
associated with combining workforce analytics with predictive HR metrics. The research also 
aimed to determine the policy implications for lawmakers and businesses. In order to explore the 
subject, the study carefully examined earlier studies and secondary data sources. The main 
findings include the importance of data quality and governance, leadership support and 
organizational alignment, collaboration and cross-functional engagement, training and 
development, piloting and iterative improvement, and continuous learning and adaptation. Policy 
implications include the need for data governance structures, training and development initiatives, 
regulatory frameworks, and incentives for innovation in order to support the adoption and 
optimization of data-driven decision-making in HRM. For businesses using data to enhance labor 
management practices and promote organizational performance in digitalized environments, this 
framework provides perceptive analysis and practical suggestions.