Skills Gap and Capacity Building for Predictive Data Analytics among Library Professionals in Michael Okpara University of Agriculture, Umudike Library

Abstract

Abstract In the era of data-driven decision-making, academic libraries are increasingly expected to harness predictive analytics to optimise collections, services, and user engagement. However, the successful adoption of predictive analytics depends not only on technology but also on the readiness and competencies of library professionals to apply analytics methods. This empirical study investigates the competency levels, skill gaps, and institutional readiness regarding predictive data analytics among libraryprofessionals at Michael Okpara University of Agriculture, Umudike Library. Findings reveal a steep decline in competency as tasks progress from basic data handling to advanced predictive analytics, indicating a significant skills gap in higher-order analytics capabilities among librarians. Based on the results, a phased capacity-building framework is proposed. The study contributes empirical evidence to the field of LIS and highlights the implications of the findings for academic libraries, professional bodies, and LIS educators.