The increasing digitization of academic libraries has generated vast amounts of data related to library collections, electronic resource usage, circulation activities, user behaviour, institutional repositories, financial transactions, and administrative operations. The effective analysis of these data has become essential for evidence-based decision making, strategic planning, and performance improvement. Data Analytics Driven Management Information Systems (DA-MIS) integrate data collection, storage, processing, visualization, and analytical techniques to provide timely, accurate, and meaningful information that supports managerial decisions. These systems enable academic libraries to optimize resource allocation, improve service delivery, predict user demands, evaluate collection performance, and strengthen institutional governance.

The present study investigates the role of Data Analytics Driven Management Information Systems in supporting decision making within academic libraries of Uttar Pradesh. The research examines the effectiveness of data analytics in collection development, electronic resource management, budgeting, user behaviour analysis, circulation management, digital repository administration, and performance monitoring. It further evaluates the influence of ICT infrastructure, analytical tools, staff competency, institutional support, and data quality on the successful implementation of analytics-driven library management systems. The study also identifies major challenges including inadequate analytical expertise, poor data integration, financial constraints, cybersecurity risks, and organizational resistance to data-driven decision making.

The investigation adopts a descriptive and analytical research design. Primary data are proposed to be collected through structured questionnaires administered to librarians, assistant librarians, faculty members, research scholars, postgraduate students, and library professionals working in selected universities of Uttar Pradesh. Secondary information will be obtained from scholarly books, peer-reviewed journals, government reports, university publications, policy documents, institutional websites, and conference proceedings. Statistical techniques will be employed to analyse the effectiveness of analytics-driven Management Information Systems in improving managerial decision making and overall library performance.