LAC Session Type
Paper
Name
KnitBI: Stitching Together Library Data with Power BI
Description

Purpose & Goals

This presentation offers a case study in the development of a centralized data platform at the University of Victoria Libraries using Power BI, and insight on how other libraries can overcome their own data-management and change-management challenges towards better data that communicates our work and impact.

Design & Methodology

Assessment at the University of Victoria Libraries has historically been distributed across units, resulting in data about our operations, services, and users being collected, structured, and managed with an unruly number of tools. This mess of data led to miscommunication, duplicated efforts, and inefficient workflows, which ultimately did not allow us to leverage our data in meaningful ways. This is an all-too-common story: academic libraries share a “data all over the place” problem. A 2016 inter-institutional study by Hurst et al., found a general need for more centralized tools. Institutions who have succeeded in developing their own centralized business intelligence tools have found this central source led to insights in cost efficiencies, customer satisfaction, collection evaluations, and more (Zucca, 2010). Given the cost and complexity of developing and maintaining a custom data platform, we searched for out of the box solutions to our data challenge. After testing reporting tools such as D3 and Tableau, we eventually selected and implemented Microsoft Power BI in 2022. Seeking to minimize disruption of established data collection practices, one reason we selected Power BI was its ability to extract existing data from a variety of storage locations and in various formats such as Excel spreadsheets, APIs, SQL databases, SharePoint folders etc., into a single location with a unified user experience. This has also allowed us to begin the switch, one data source and subject at a time. The technical element was only half the battle; discovering how to implement change across library units without causing undue disruption was the longer process. This included inventorying our current data assets, tools, and storage locations across units; consulting with units to understand business needs; participating in the data analysis community across campus; partnering with user experience experts; and providing training to build up data literacy across the organization.

Conclusions

In practice, centralizing our data holdings has streamlined our routine statistical reporting (e.g. to CARL, ACRL, etc.) so that we can now focus our efforts on areas of greater impact. The tools we’ve developed have allowed us to harness our data in new ways and ask new questions to demonstrate our impact. For example, our recent work to analyze the library’s investment in Open Access or partnerships in faculty research grants – areas that go beyond the traditional annual report and which speak to the greater mandate of our institutions. However, with centralized assessment, this work can only be achieved through close partnerships with the data holders and users. We’ve learned that developing tools and visualizations that serve both the creator and end user requires: 1) understanding the data literacy level of your audience, 2) creating an overarching user experience, and 3) developing training in parallel to launching new tools. While securing buy-in to this new approach can prove challenging, our approach has been to start small by working with colleagues who have clearly articulated questions in mind, and who understand the value of their data. Thus, demonstrating successful collaborations will build trust and understanding across the organization from which to build on.

Implications & Value

As higher education institutions face greater fiscal pressures, their libraries must respond by embracing a culture of data-driven decision making, which requires greater data literacy for all library staff. We’ve heard these talking points before to this seemingly universal challenge — so how can we move beyond platitudes and into action? Central data dashboards, in practice, can improve statistical reporting, decision making, and communicating library impact. Our work with Power BI has demonstrates that it is possible for libraries to move to a unified source of data without developing and maintaining custom applications or disrupting routine. All this to say, this is no small task and requires investing in technical and data skills for staff, and, above all, someone with these skills who can develop tools, lead professional development, and liaise with subject experts. Hurst, M., Madsen, C., Wilson, F., Smith, M., & Garrity, W.F. (2016). All Your Data Displayed in One Place: Preliminary Research and Planning for a Library Assessment Dashboard and Toolkit [Paper presentation]. Proceedings of the 2016 Library Assessment Conference, Arlington, Virginia. http://old.libraryassessment.org/bm~doc/91-wilson-2016.pdf Zucca, J. (2010). Data Farms or a Field of Dreams? [Paper presentation]. Proceedings of the 2010 Library Assessment Conference, Baltimore, Maryland. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=5a9aaa7ff659187b90f29f48fa025774e720fbb2

Keywords
Power BI, data visualization, data hub, change management, centralized dashboard, data management