Reposted with permission from AALL Spectrum, Volume 23, Number 1 (September/October 2018), pgs. 20-23.
By Amy Atchison, Associate Law Librarian for Public Services at the University of California Irvine School of Law Library, and June H. Liebert, Firmwide Director of Library & Research Services at Sidley Austin LLP
We both started as research librarians at the University of California Los Angeles Law Library more than 20 years ago. Back then, we tracked reference desk requests each day with tick marks on a sheet of paper. We refer to this now as “data gathering for dummies.” While this simple method recorded the number of requests in a given time period, it provided almost zero value (how often do tick marks ever get aggregated?), and we lost the most useful information, such as the questions and the answers.
Faculty submitted requests almost exclusively via email or phone back then, which is still true today. Our biggest innovation at the time was to print each unassigned faculty request and tape it to an old file cabinet with the idea that a visual of our growing workload would encourage us to take more requests. The unintended effect was group avoidance of the file cabinet and a de facto game of chicken with the faculty requests.
Time passed and things got better. Although we now work at two very different institutions—a law school and a law firm—both of our libraries must run efficiently and cost-effectively while still exceeding our users’ expectations. One way we accomplish this is by tracking research requests in an online system that provides us with the data we need to better understand our users, staff, and organizations.
Data analytics offer benefits for all law libraries by providing both broad and detailed views of operations and areas where productivity has grown or lagged. The challenge is finding a system that tracks work, provides useful measurements for managers, and is easy to use (i.e., a system that does almost everything that we need).
There are many ways to track the number of questions your library receives (including those paper tick marks), but today’s sophisticated online tracking systems provide so much more. The trick to selecting the right system is to first identify your needs and then
prioritize them. This determines the data you should collect and will help you select a system that will meet most of those needs.
What Is a Reference Tracking System?
Reference tracking systems are usually web-based systems that manage and store research requests in a single location. A requestor simply sends an email to the library request address and the tracking system automatically creates a request ticket (phone calls and in-person requests may be manually added). Depending on the request, one or more researchers can add themselves to the ticket as the assignee(s). The assignee then responds to the request from within the system, including acknowledging the request, asking for more information, or sending the research results to the requestor. Ideally, the system records all interactions related to the ticket, including the requestor’s and assignee’s names, the date of the request, the completion date, and the time worked, and it stores or links to the work product.
These systems also provide preset reports, graphs, and other data visualizations that help with analyzing the data on the back end. Some systems also allow you to create custom reports for more flexibility. However, almost all systems allow you to export the data into other formats for use in spreadsheets and data visualization tools, such as Tableau.
How Can Reference Data Be Used?
Reference tracking systems can improve management of workload by producing
meaningful data about user needs, which can then be analyzed to identify trends and patterns. They also serve as a repository of easily accessible, completed requests. Here are just a few examples of how we use our reference tracking systems to help with
- To ensure adequate staffing: At Sidley’s library, we discovered spikes in the number of requests during the last 10 minutes of the workday in each time zone, which explained why we had some difficulty providing adequate staffing in the late afternoons. Based on this data, we created an extra shift to help cover this time period, which has greatly improved our ability to manage our late afternoon work.
- To better understand how our work has changed: At Sidley, the number of research requests has steadily risen over the last four years (see Figure 1). We also receive increasingly complicated requests, as indicated by the number of hours spent per request and the type of requests received. This data gave us a much more holistic view of the work we do (especially when compared to a simple count of the number of requests received).
- To pinpoint inefficiencies: At the University of California, Irvine (UCI) School of Law Library, we use our tracking system to identify projects we should delegate to lower-cost employees, such as faculty research assistants. For example, we are currently tracking the frequency of and time spent on 50-state survey requests.
- To identify training opportunities: The bubble graph image on the following
page (Figure 2) displays the requests handled by three different researchers. The color of each bubble represents a different type of request, such as document delivery, case law research, company profiles, securities research, etc. The size of each bubble represents the number of requests taken. Depending on the overall organizational goals, such graphs can help identify where there may be opportunities for further growth.
- To value the library’s contributions to the organization: Tracking hours spent on work for specific projects, people, and cases helps put a dollar value on our work and tells us how resources are used. At UCI, we include hours spent on research for new and tenure-track faculty in our annual report to the dean, and in future reports we intend to include data on services provided to law school clinics, pro bono work, and other law school departments.
- To justify resource requests: The tracking system enables us to document the number of user requests for a particular resource not currently in our holdings. At Sidley, we tag and note requests for these resources so we can quickly find these tickets when asked to justify subscribing to them (or not).
- To identify users’ future needs: An online system can help you quickly spot trends in specific types of requests so you can plan for needed resource shifts. For example, increased requests for statistical research may mean that you use a new job opening to hire someone with more experience in this area. This type of hard evidence can also provide valuable support for additional resource requests.
- To preserve knowledge and encourage information sharing: A great tracking system should record all written interactions between the requestor and librarian for each transaction, so it serves as a knowledge-base for your staff (and potentially for your users). This is particularly helpful to new researchers, who can use the system to identify similar requests and learn from what was done by other researchers.
When Should You Start Collecting Data?
The best time to start collecting data is right now, because you are losing valuable data every day. A simple way to get started immediately is to use a spreadsheet that all of your researchers can access. Choose a few questions you want to answer and start collecting
the data you need to answer them. As long as your data entry is consistent (we highly recommend drop-down codes), you can upload the data once you have selected and set up a full-blown research tracking system.
While implementing a more sophisticated system can seem daunting, we have found it to be well worth the time and effort. A lesson we have learned over the years is that success depends on understanding ourselves, individually and as an organization. We may be at different libraries now, but we still face the same practical challenge of how to use the
lessons of prior work to predict future needs and reach our intended goal of exceptional research service. Collecting research data is the place to start.
Authors Amy Atchison and June Liebert, along with Christina Tsou, UC Irvine School of Law Library, did a presentation related to this topic at the 2018 AALL Annual Meeting. AALL members can listen to a recording of the program “Reference Analytics for Data-Driven Decision Making” at bit.ly/AM18RefAnalytics.