What metrics are most useful for comparing journal packages?

What data visualizations enable the most insight into the value of these packages?

How can libraries produce these kinds of reports, including data visualizations, as efficiently as possible?

Over the last several years at Minnesota State University, Mankato, we have iteratively developed standardized reports for journal collection development, outreach, and academic program support. We previously presented an early version of our package-level analysis reports, where we focused on how to use Tableau for data visualization. Now, we will demonstrate new and improved reports, with new package-level and subject-level metrics leading to additional insights, and we’ll highlight why we prefer Python/ Jupyter Notebook for data visualization. We will also stress why it is important to develop package-level analysis and comparison capabilities beyond what can be provided by UnSub or the library management system.

In addition to talking about the applications of these reports for collection development, we’ll discuss how these reports contribute to a new liaison outreach project. The goals of this new project are (1) to re-affirm the value of the journal packages, (2) to prioritize them for continuing investment, and (3) to garner testimonials. Alongside ‘elevator speech’ versions of our reports, these testimonials can be shared with our university administration in order to drive home the importance of an adequate budget to support the curriculum and student success.