Making The Invisible, Visible: Using Data Storytelling To Promote Systems Work In The Academic Library

Systems and technology work often remains invisible within academic libraries, despite supporting nearly every service relied upon for a campus’ information needs. This presentation demonstrates how a data-driven storytelling approach can surface that hidden labor and communicate its value to institutional stakeholders. At Bridgewater State University’s Maxwell Library, the Systems department developed an internal ticketing system using Microsoft Teams and SharePoint Lists to centralize systems questions and issues across a wide suite of applications. This process is embedded in programs and digital spaces already trafficked by library employees, making it a seamless process to adopt. The ticketing system is paired with a Power BI dashboard that automatically refreshes and visualizes ticket data to promote the work being done in the department. The resulting visualizations reveal patterns in request types, turnaround times, recurring issues, and the overall workload of systems support.

Framing these operational metrics as a narrative has enabled effective communication with the library and campus administrators. The dashboard now serves as evidence for documenting the Systems department's contributions to institutional priorities and ability to meet strategic plan benchmarks in ways that were unable to be communicated before. It also positions systems staff as data professionals whose work sits at the center of helping knowledge flow through the campus.

The session will outline practical steps for building similar workflows, including identifying meaningful data points, designing visualizations that speak to different audiences, and setting up workflows that take little maintenance. Attendees will gain strategies for transforming routine service data into compelling stories that advocate for systems work and reinforce the library’s role in the wider institutional data ecosystem at a time when this work is often undervalued.