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.
