Semantic Data Virtualization for Real-Time HR Decision Intelligence in Agile Enterprises

In today’s fast-moving organizations, HR staff need to make on-the-fly, data-informed decisions—be it detecting early burnout, planning skill-based rotations, or optimizing recruiting pipelines. This paper presents a lightweight Semantic Data Virtualization (SDV) framework, adapted for HR decision intelligence, which integrates multiple structured HR sources— such as the IBM HR Analytics Employee Attrition dataset and an external learning API—into a single semantically rich layer. By mapping core HR concepts (performance scores, roles, learning activities) to a shared ontology and enabling graph-based, real-time access, the SDV layer allows decision-makers to query live data in natural language and view trends instantly. A pilot study demonstrates up to 60% reduction in data integration overhead, 25% improvement in attrition-prediction accuracy, and 40% faster insight delivery compared to a baseline ETL system. Evaluated within a controlled HR decision scenario, the proposed approach underscores the potential of semantic virtualization as a foundation for agile, real-time HR decision intelligence.