What’s in an Address?

Transforming ILLiad Data into Resource Sharing Insights

Authors

DOI:

https://doi.org/10.23974/ijol.2025.vol10.4.569

Abstract

Interlibrary loan (ILL) systems contain a wealth of data that can inform operations, assessment, and collection development. Yet, the capacity to analyze and interpret ILL activity often lags behind other library departments because of legacy software. ILLiad, one of the most widely used ILL management systems, contains numerous free text fields, including address records originally designed for mailing rather than for analysis. Inconsistent and incomplete entries limit the ability to extract meaningful statistics about resource sharing activity. To answer nuanced questions, including those required by the ALA RUSA STARS Quadrennial International Interlibrary Loan Survey, we developed a simple, replicable three part method: Clean, Create, and Group. By cleaning address data using a controlled vocabulary, creating internal-use addresses to represent local workflows, and grouping records by category, we transformed a static contact list into a dynamic data source. This project enhanced our ability to generate targeted statistics, improved collaboration across departments, and demonstrated how a modest data-cleaning initiative can yield lasting operational and analytical benefits.

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Author Biography

  • Joanne McIntyre, University of Colorado Boulder

    Joanne McIntyre is the Resource Sharing Librarian at the University of Colorado Boulder, where she manages the Resource Sharing Section. She is the co-chair of the ALA RUSA STARS International Interlibrary Loan Committee. Her work focuses on interlibrary loan data quality. She presented this project as a poster at the 2025 American Library Association Conference.

References

Atlas Systems. (n.d.). ILLiad user documentation. https://support.atlas-sys.com

Knievel, J. E., Wicht, H., & Connaway, L. S. (2006). Use of circulation statistics and interlibrary loan data in collection management. College & Research Libraries, 67(1), 35-4.

Showers, B. (Ed.). (2015). Library Analytics and Metrics: Using Data to Drive Decisions and Services. Facet.

Tennant, R., & McCue, P. (2023). Making sense of the lending fill rate in interlibrary loan: External factors we must control for. College & Research Libraries, 84(4), 562-581.

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Published

2025-12-16

Issue

Vol. 10 No. 4 (2025): Assessment in Libraries

Section

Reports From the Field

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How to Cite

What’s in an Address? Transforming ILLiad Data into Resource Sharing Insights. (2025). International Journal of Librarianship, 10(4), 149-158. https://doi.org/10.23974/ijol.2025.vol10.4.569