Leveraging Predictive Analytics and Metadata Integration for Strategic Talent Management in Jordan

IntroductionTalent management is critical for organizational performance. This research explored the use of predictive analytics on employee metadata, including profile analysis, performance history, training records, and career progression scores, to optimize retention and promotion strategies in Jordanian organizations. The study provided insights into the effectiveness of integrating predictive tools with metadata to enhance talent management outcomes. MethodsA quantitative research design, incorporating descriptive and correlational approaches, was employed. Data were collected from 257 HR professionals and decision-makers using structured questionnaires and organizational records. Statistical techniques such as linear and logistic regression, correlation analysis, and machine learning models were used to examine the predictive influence of variables like age, training hours, performance ratings, and career progression scores. ResultsThe results indicated that training hours, performance ratings, and career progression scores are good predictors of retention rates while age and tenure were strong predictors of success promotion. Machine learning models strongly predicted retention outcomes with an attainment of an R-squared score of 0.671, so predictive analytics can enhance efficiency in decision-making. Moderate use of Predictive analytic tools was related to improved promotion outcomes suggests a balance between data-driven and human judgment approaches. ConclusionThe study contributed to the growing discourse on data-driven HR practices, contextualizing findings within Jordanian organizations. It highlighted the ethical and cultural considerations necessary for implementing metadata-driven tools. The results underscored the potential of predictive analytics to improve talent management processes, ultimately supporting the achievement of strategic HR goals