📄 Abstract
Numerous technical innovations have taken human resource management to a whole new level using data strategies which have fueled decision-making and organizational alignment through the use of advanced analytic techniques and predictive modeling. The following study exposes a comprehensive framework, which thrives on the integration of data analytics into the major functions of the HR department such as talent acquisition, engagement, performance management, and retention by using various data sources to draw actionable insights and improve decision-making. Focusing on HR data centralization and the implementation of evidence-based practices, the framework tackles the main obstacles such as data security, algorithmic discrimination, and change resistance, while at the same time, it suggests the best practices for a successful implementation. Thanks to the use of novel technologies such as AI and machine learning, companies are able to elevate the conventional HR management process to predictive models driven by a completely proactive approach that can anticipate trends and consequently reduce threats. Through this method, not only are operational efficiency and employee satisfaction ameliorated, but new models of innovation, sustainability, and a long-term competitive advantage in an ever-changing business environment are also developed.
📚 How to Cite:
Tawfeeq Hasan , OPTIMIZING HR PERFORMANCE WITH DATA-DRIVEN STRATEGIES: A COMPREHENSIVE FRAMEWORK , Volume 12 , Issue 7, July 2026, EPRA International Journal of Multidisciplinary Research (IJMR) , Pages: 1045 - 1052 , DOI: https://doi.org/10.36713/epra28789