focus: using data to aid decision-making


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Presented at the Futures Forum in Sydney, Australia, at the State Library of New South Wales, 4/5/11.

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  • A note about EKU:16,000 FTE students, regional comprehensive university, 23 or so librarians and as many support staff.
  • A note on quantity vs quality. This presentation is only about using quantitative data to make decisions. Qualitative data analysis yields different information that can also be used in decision-making.
  • Most libraries keep track of basic library use statistics; how many visitors there are, how many items are circulated, how many reference transactions take place and the like. Most academic libraries also collect and report how much library instruction is done and look periodically how collections are being used. How can libraries use visualization tools to cast these numbers in a different light and challenge assumptions or aid decision-making? Things we know:Reference transactions: There was a precipitous decline from FY2004 to FY2008, followed by a steady increase after we added a chat/TXT reference service. Instruction attendance: Steady drop since 2005; more precipitous drop from FY2008 to FY2009 due to transitioning face-to-face General Studies instruction to online tutorials. Questions for thought:What questions can we ask that could be answered by this data? What questions cannot be answered by this data?What overall patterns exist? Do decreases or increases occur simultaneously? Why or why not? The above graph plots library use as it has been traditionally defined. How has library use changed in recent years? What should be counted? Are we collecting data that reflects that use?
  • Other ways of visualizing data Consider this photo of 2007 art installation in Paris’ l’eglise St. Paul by Robert Stadler (left). Do the lights form a pattern?
  • Consider the view of the lights from the main entrance of the church (right).Thinking about and visualizing data is similar; if data is presented or visualized in a new way, a new pattern may emerge. Can we visualize library data so that new patterns emerge? Are assumptions confirmed or challenged?
  • A picture of library use over the academic year From bottom to top: instruction sessions; reference transactions; circs; off-campus website hits; gate count; website hits.Consider this area graph displaying one year of library data. We know that September and April are our busiest months; charting the data proves that. The academic year definitely adheres to a cycle. Visualizing library activity in this way might help the library administration allocate resources more effectively, or it may help library staff in project planning. Questions for thought:Where do traditional library services fall on this graph? Given a budget cut or other shortfall of resources, what services provide the most bang for the library buck? Can this question be answered here?What could account for the great gap between gate count and the number of uses of traditional services?
  • The graph at the top reflects the fact that traffic to the library website drops dramatically on the weekends and is mostly from the United States. Here are the same graphs that compare the traffic to the library website during the 2008-2009 and 2009-2010 academic years:
  • Each point on the above graphs represents a week. Weeks that include short breaks, like fall break and the Thanksgiving holidays, dip some, but it’s clear that the entire university forgets about academic work during the first two weeks of winter break.  For thought:Times of very low in-person use of library resources are obviously times when library staff can be allocated to do other things, or when open hours can be reduced. Brooks-Kieffer talks of macroevaluation and microevaluation. The above is clearly macroevaluation; how could this data be used to target areas for microevaluation?In this course, we are covering quantitative data, but effective assessment of services requires the collection and analysis of qualitative data as well. Qualitative data in libraries can be collected through formal surveys like LibQUAL+ or through informal surveys, focus groups or usability testing.
  • SFX menu.. Explainopenurl path from database to menu to target (FT, ILL, catalog)
  • Most-frequently presented targets (requests) and how often users click on them
  • Adding qualitative data: collection statements for weeding project
  • focus: using data to aid decision-making

    1. 1. focus<br />using data to aid decision-making<br />cinditrainor<br />library technology & data services<br />eastern kentucky university libraries<br /><br />
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    12. 12. English: This department has a large enrollment and typically over 100 majors. A high number of Gen Ed courses and the timeliness of the subject mean that a book collection that supports scholarship is essential. Focus of weed will be on zero-use, non-essential volumes.<br />
    13. 13. Cindi Trainor<br />EKU Libraries<br /><br /><br />