Identifying library data sources

This guide lists some of the sources of attention data that are available in libraries.
The problem
Libraries wishing to build a picture of user attention may face the challenge of identifying the appropriate data. This, in turn, depends on the purpose of use. The latter may range from collection management (clearing redundant material, building ‘short loan’ capacity), through developing recommender services (students who used this also used that, searched for this retrieved that, etc), to providing student success performance indicators.
Libraries use a range of software systems through which users interact with premises, services and resources. The library management system (LMS) system is far from the only source of data, and the OPAC and the LMS circulation module represent increasingly partial views of user attention, activity and usage.
Breaking the problem down. In this guide considers some of the variety of sources available within library services. You may have additional sources to consider.
Some typical data sources
Libraries already working with activity data have identified a range of sources for the purposes of Collection Management, Service Improvement, Recommender Services and Student Success. Potential uses of data will be limited where the user is not identified in the activity (flagged as 'no attribution' below).
Some key examples are:
Data Source
What can be counted
Value of the intelligence
Visits to library
Service improvement, Student success
Virtual visits to library (no attribution)
Service improvement
Searches made, search terms used, full records retrieved (no attribution)
Recommender system, Student success
Books borrowed, renewed
Collection management, Recommender system, Student success
URL Resolver
Accesses to e-journal articles
Recommender system, Collection management
Counter Stats
Downloads of e-journal articles
Collection management
Reading Lists
Occurrence of books and articles - a proxy for recommendation
Recommender system
Help Desk
Queries received
Service improvement
Further consideration
Here are some important questions to ask before you start to work with user activity data:
  • Can our systems generate that data?
  • Are we collecting it? Sometimes these facilities exist but are switched off
  • Is there enough of it to make any sense? How long have we been collecting data and how much data is collected per year?
  • Will it serve the analytical purpose we have in mind? Or could it trigger new analyses?
  • Should we combine a number of these sources to paint a fuller picture? If so, are there reliable codes held in common across the relevant systems – such as User ID?
Additional resources