Talent Pool Analysis and Screening for Lecturers Based on Data Management for Business Intelligence at University
Abstract
This study aims to develop a comprehensive understanding of a Talent Pool Analysis and Screening for Lecturers model based on data management and business intelligence (BI) as a strategic approach to improving lecturer recruitment and academic workforce management in universities. The core problems identified include the limited integration of academic and employment data, the predominance of subjective assessments in lecturer selection, and the suboptimal use of predictive analytics to support accurate and forward looking human resource decisions. This research proposes the application of HR analytics, people analytics, and BI as a solution to create a more objective, efficient, and evidence based lecturer screening process. The objects of analysis consist of scholarly literature on HR analytics, BI, and AI/ML for recruitment, as well as institutional documentation related to lecturer management policies. Employing a descriptive qualitative method through literature review and document analysis, the study finds that BI technologies and predictive models significantly enhance candidate identification accuracy, accelerate the selection process, and enable more strategic academic workforce planning. The findings also indicate that the successful implementation of a BI based model requires data readiness, analytical competence, and governance frameworks that ensure ethical algorithmic use. The study concludes that integrating BI into academic talent management not only strengthens recruitment effectiveness but also provides a foundation for developing new theoretical insights, such as an Academic Talent Intelligence framework, to support more adaptive and future oriented lecturer management practices.
Keywords: Talent Pool; Lecturer Recruitment; Data Management; Business Intelligence; Higher Education; Human Resource Analytics; Decision Support System.
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