Predictive Analytics for Library Resource Usage: A Data-Driven Approach to Enhancing Services

Author(s): SMT. Ramyia Shreejesh

Abstract:

Artificial Intelligence and AI-driven tools have changed the landscape of libraries and its services. Libraries have transformed from mere service-provider to the data-driven forwarding thinking innovative hubs. The present research paper focuses on the concept of predictive analytics and how it can be useful to libraries. The paper explores the potential of predictive analytics in anticipating user needs, resource allocation, proactive personalised services, and overall enhancing library experiences. This paper studies the concept, applications, data-sources, relevance, challenges, and future implication of predictive analytics on libraries. It represents a transformative shift for libraries, moving from traditional reactive bodies to a proactive and data-driven innovation hubs. It uses statistical modelling, machine learning, and artificial intelligence to forecast future outcomes, provides a powerful framework for strategic decision-making in library contexts. Its applications are far-reaching, spanning core functions such as collection development, user services, and institutional planning. By anticipating user needs and forecasting resource demands, libraries can optimize their budgets, personalize the patron experience, and demonstrate their critical value to stakeholders and funding bodies. The study focuses on the premise that Predictive analytics enables evidence-based decision making, making it a critical tool for 21st Century libraries.

keywords:

Pages: 59-62     |    2 View     |    0 Download