Leveraging Organizational Networks: Data driven decision making to Enhance Predictive Analytics for Employee Attrition

As organizations seek data-driven talent management, there is a growing need for more accurate and comprehensive predictive analytics to address employee attrition of the top talent. Conventional HR metrics often lack the depth required to manage this challenge effectively. This study introduces a novel approach to people analytics by leveraging network-based data science to capture organizational social dynamics. Using a dataset enriched with social network data from the structural characteristics of interaction histories and teamwork dynamics, this research aims to develop a network-based predictive model for forecasting attrition intent of the top talent. The model incorporates network centrality, influence, and interconnectedness of the employees. By integrating these features with graph theory, our network-enhanced model significantly outperforms traditional methods in predicting employee attrition and optimizing organizational decision-making.