Optimization of University ICT Resource Allocation Using Big Data in Modibbo Adama University, Yola, Adamawa State
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Background of the Study
Efficient allocation of ICT resources is crucial for ensuring that universities remain competitive and provide high-quality educational services. At Modibbo Adama University, Yola, Adamawa State, the traditional methods of resource allocation are increasingly being challenged by the rapid expansion of digital infrastructure and the growing demand for technology-enabled learning environments. Big data analytics offers a transformative approach to optimizing ICT resource allocation by analyzing extensive datasets that include usage patterns, network performance metrics, student and faculty feedback, and historical expenditure data (Olu, 2023). These analytics enable decision-makers to identify underutilized resources, forecast future demands, and reallocate funds to areas that require improvement. Moreover, predictive models can assist in anticipating maintenance needs and potential system failures, thereby minimizing downtime and enhancing operational efficiency (Adebayo, 2024). The use of big data also promotes transparency and accountability in resource management, as decisions are based on quantifiable data rather than subjective assessments. However, the implementation of big data strategies in ICT resource allocation faces challenges such as data integration from disparate sources, ensuring data accuracy, and addressing concerns related to data security and privacy (Balogun, 2025). This study aims to evaluate the effectiveness of big data analytics in optimizing ICT resource allocation at Modibbo Adama University by comparing current practices with data-driven strategies, ultimately providing recommendations to enhance the efficiency and effectiveness of ICT management (Olu, 2023; Adebayo, 2024; Balogun, 2025).
Statement of the Problem
Modibbo Adama University currently relies on traditional, manual methods for ICT resource allocation, which often result in inefficient utilization and delayed responses to infrastructure needs. The lack of an integrated data-driven approach means that decisions regarding resource distribution are based on incomplete and outdated information, leading to suboptimal performance and wastage of funds (Olu, 2023). Although big data analytics promises to provide comprehensive insights into resource usage and demand, its implementation is impeded by challenges such as data fragmentation, inconsistent data quality, and security concerns (Adebayo, 2024). Additionally, the absence of predictive analytics in the current system limits the university’s ability to anticipate future requirements and proactively address potential issues. These deficiencies contribute to frequent system downtimes and reduced user satisfaction among students and staff. Without a robust framework for data-driven ICT management, the university risks falling behind in technological advancements and failing to meet the evolving needs of its academic community. This study aims to bridge the gap by developing a big data-based model for ICT resource allocation, comparing it with existing practices, and proposing strategic improvements to enhance overall efficiency and accountability (Balogun, 2025).
Objectives of the Study:
• To develop a big data-driven model for ICT resource allocation.
• To compare the performance of data-driven strategies with traditional allocation methods.
• To recommend strategies to improve data integration and resource management.
Research Questions:
• How does big data analytics improve ICT resource allocation?
• What are the limitations of traditional resource allocation methods?
• How can data integration and security be enhanced in a big data framework?
Significance of the Study
This study is significant as it explores the application of big data analytics to optimize ICT resource allocation at Modibbo Adama University, aiming to enhance operational efficiency and support academic excellence. The insights will assist administrators in making informed, data-driven decisions, thereby improving technology utilization and reducing costs (Olu, 2023).
Scope and Limitations of the Study:
This study is limited to the evaluation of ICT resource allocation at Modibbo Adama University, Yola, Adamawa State.
Definitions of Terms:
• Big Data Analytics: The process of examining large datasets to derive actionable insights (Adebayo, 2024).
• ICT Resource Allocation: The distribution and management of information and communication technology resources (Olu, 2023).
• Predictive Analytics: Techniques used to forecast future trends based on historical data (Balogun, 2025).
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