Optimization of University Library Resource Utilization Using Data Science in Federal University Dutse, Jigawa State
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Background of the Study
University libraries are central to academic success, yet many struggle with underutilized resources and inefficiencies in operations. At Federal University Dutse, Jigawa State, traditional library management systems have been challenged by the exponential growth of digital resources and changing student needs. Data science offers innovative techniques to analyze usage patterns, inventory data, and user feedback to optimize resource allocation and improve service delivery. Advanced analytical methods—such as clustering, predictive modeling, and visualization—can reveal hidden trends in circulation, highlight popular resources, and forecast future demand (Adeyemi, 2023). By harnessing these techniques, library managers can make informed decisions regarding acquisitions, digital resource investments, and space management. Furthermore, integrating data science into library operations enables real-time monitoring of user behavior, which can drive strategic initiatives like targeted marketing and personalized services for diverse student groups (Olu, 2024). However, despite these advantages, several challenges persist, including data integration from legacy systems, ensuring data quality, and protecting user privacy. The implementation of data science methodologies requires robust IT infrastructure and specialized training for library staff. This study therefore aims to investigate how data science can optimize resource utilization, reduce wastage, and enhance user satisfaction at Federal University Dutse. By comparing current practices with data-driven strategies, this research seeks to offer actionable recommendations that could transform library management practices, improve operational efficiency, and support the university’s academic mission (Balogun, 2025).
Statement of the Problem
Federal University Dutse’s library system currently faces inefficiencies due to outdated manual processes that result in poor resource utilization and a lack of responsiveness to user demand. Traditional management techniques, while familiar, often lead to overstocking in some areas and shortages in others. Additionally, the absence of a real-time monitoring framework means that trends in resource usage are not promptly identified, leading to suboptimal decision-making. Although data science has the potential to revolutionize resource allocation by processing vast amounts of data, its integration is hindered by challenges such as data fragmentation, low data quality, and concerns over user privacy. These issues are compounded by limited technical expertise among library staff and resistance to change from conventional practices. Without a systematic, data-driven approach, the university risks continued inefficiencies that negatively affect student satisfaction and academic performance. This study aims to bridge this gap by evaluating how data science methods can enhance library resource utilization, ensuring that investments in library infrastructure and collections are optimally aligned with user needs (Adeyemi, 2023; Balogun, 2025).
Objectives of the Study:
• To develop a data science model for optimizing library resource utilization.
• To assess the impact of data-driven strategies on resource allocation and user satisfaction.
• To recommend practical measures for integrating data science into library management.
Research Questions:
• How can data science techniques improve library resource utilization?
• What impact do data-driven strategies have on user satisfaction and resource allocation?
• What challenges must be overcome to successfully implement these techniques?
Significance of the Study
This study is significant as it investigates the potential of data science to optimize library resource utilization at Federal University Dutse, offering insights that can enhance decision-making and improve user satisfaction. The findings will provide actionable recommendations for transforming library management practices, ultimately contributing to a more efficient and responsive academic support system (Adeyemi, 2023).
Scope and Limitations of the Study:
The study is limited to optimizing library resource utilization at Federal University Dutse, Jigawa State, focusing on data integration, analysis, and practical implementation challenges.
Definitions of Terms:
• Data Science: The use of advanced analytical methods to extract insights from large datasets (Olu, 2024).
• Resource Utilization: The effective and efficient use of library materials and services (Adeyemi, 2023).
• Predictive Analytics: Techniques used to forecast future trends based on historical data (Balogun, 2025).
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