Real-Time Decision Dashboards Using Data Science in Library Admin

Abstract

The incorporation of data science into library management provides a more effective approach through the use of intelligent dashboards, enabling real-time decision-making. As such, this work highlights the design and implementation processes of decision dashboards, designed to enable library administrators to optimize operational processes, enhance resource allocation, and improve user engagement metrics in real-time. These dashboards automate decision-making processes with actionable insights that leverage predictive analytics derived from circulation logs, user behavior data, digital resource access logs, and spatial monitoring of resource utilization. To sustain adaptive and proactive library management, the system utilizes machine learning for demand forecasting, anomaly detection, and trend analysis. A case study approach was employed to evaluate the impact of dashboard implementation on a university library, yielding increased operational and service delivery efficiency, enhanced service user satisfaction, and improved operational transparency for users. The emphasis on academic libraries highlights the underutilized opportunities available for governance and presents a strategic approach to customizable dashboard design tailored to accommodate varying institutional frameworks. It is elementary that the incorporation of real-time data science tools into library administration serves as a pointer towards the responsive nature of modern information systems, paving the way for profound evolutionary shifts in user-centric, responsive, innovative library systems structured around contemporary information ecosystem needs.