LEVERAGING PREDICTIVE PEOPLE ANALYTICS TO OPTIMIZE WORKFORCE MOBILITY, TALENT RETENTION, AND REGULATORY COMPLIANCE IN GLOBAL ENTERPRISES

As globalization, digital transformation, and regulatory complexity reshape the human capital landscape, largeenterprises face increasing pressure to manage workforce mobility, retain high-value talent, and maintaincompliance across jurisdictions. Traditional HR approaches often fall short in addressing these evolving demandsdue to their reactive nature and fragmented data sources. This paper examines how predictive people analyticscan be harnessed as a strategic lever to optimize workforce decisions and align them with enterprise-wideperformance, mobility goals, and legal obligations. By integrating advanced data modeling techniques includingmachine learning, sentiment analysis, and attrition forecasting organizations can identify early signals of flightrisk, engagement decline, and compliance vulnerabilities at both local and global levels. Particular attention ispaid to modeling cross-border mobility patterns, capturing cultural and operational factors that influencerelocation success, and ensuring compliance with region-specific labor laws and privacy regulations. The studyalso highlights the role of data governance, ethical AI deployment, and cross-functional collaboration in unlockingthe full potential of predictive HR systems. Through real-world enterprise use cases and a proposedimplementation framework, the paper offers actionable insights for HR leaders, compliance officers, andworkforce strategists aiming to build more resilient, responsive, and compliant global talent ecosystems.Ultimately, predictive people analytics is positioned not merely as a reporting tool, but as a critical enabler ofstrategic foresight in human capital planning and global workforce optimization.