Data-driven decision making in human resources to optimize talent acquisition and retention

Abstract

This review paper examines the impact of data-driven decision making on optimizing talent acquisition and retention in human resources. The objective is to synthesize existing research and provide a comprehensive overview of how data analytics can enhance HR functions. By analyzing a wide array of studies and industry reports, we explore the various data-driven methodologies employed in HR, such as predictive analytics, machine learning models, and advanced data visualization techniques. Key findings from the literature reveal that organizations leveraging data analytics in their HR processes achieve more efficient and effective hiring and retention outcomes. Predictive analytics and machine learning models facilitate the identification of high-potential candidates and align them with appropriate roles, thereby decreasing time-to-hire and reducing recruitment costs. Additionally, data-driven insights into employee behavior, satisfaction, and engagement are critical in developing targeted retention strategies, resulting in improved employee loyalty and reduced turnover rates. The review highlights the transformative potential of data-driven decision making in HR, emphasizing the need for continuous investment in data analytics infrastructure and capabilities. By adopting a data-centric approach, HR professionals can better navigate the complexities of talent management and foster a more dynamic and responsive workforce. This paper concludes that integrating data analytics into HR practices is essential for optimizing talent acquisition and retention, ultimately contributing to organizational success and competitiveness.