Interlibrary Loan Data Analysis and Visualization

I’m working on improving the interlibrary loan services at North Park as well as improving my skills in statistics and data visualization. I’ve combined these two interests to look at analyzing and visualizing our interlibrary loan data using Tableau. 

Data Sources

The first step was identifying and “cleaning” my data sources. I am pulling in data from ILLiad that includes the create date of the request, the patron status at my institution (undergraduate, graduate, etc.), and more information. I exported the relevant requests (Borrowing and DocDel) from ILLiad and then cleaned them up in EXCEL.

  • I removed all of the personal information (names, emails, etc.) because this data is being displayed on a public Tableau website.
  • I deleted empty columns and information that I didn’t need to examine. This hopefully speed up the load time in Tableau.
  • I reformatted the “Photo Journal Year” feature to make it more readable.

I also needed to get information from OCLC. I’m looking at the

Tableau allows you to do a somewhat primitive “join” feature and that’s what I’m doing here. I’m doing a left join that matches on ILL Number in both sheets. It includes all the ILLiad request information and only the OCLC information that matches on ILL Number. I suppose I could do a full join…but I’m sticking to this approach for the moment.

Data Questions

I am starting my analysis with questions and first wanted to get a general overview of the data. This overview contains questions like:

  • What is the source of these requests?
  • Who is making these requests? What is the institutional status of the requester (undergraduate, graduate, etc.)?
  • What are people requesting? Do the requests “clump” in a given area and/or time period?
  • What is the average turnaround time?
    • How long does it take from the moment of request creation for the request to be processed?
    • For the right library to receive that request?
    • For that library to fill the request?
    • For the item to arrive in the mail?

These questions about turnaround time are leading me in a new direction – one with a new set of questions and new set of data sources – and I’ll leave that for another day.

Overall, I think this looks pretty good. Tableau is a great way to get an overview of the data and it provides insights about usage and patterns that should be actionable with just a little work. I’ll update that workbook with any changes and hope to provide fresh data over the summer.

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