Upgrade to Pro — share decks privately, control downloads, hide ads and more …

What is a data-driven Academic Library?

What is a data-driven Academic Library?

This webcast will give an overview of how academic libraries are using data to drive decisions. The presentation will touch on the terminology, tools, services, and data available to libraries and provide real world examples of how academic libraries have used data to enhance services, improve online tools, and develop collections. Sarah Tudesco will also touch on how to communicate data to various stakeholders. Attendees will depart with with a number of resources to learn more including the best books, blogs, journals, Twitter handles and more to continue learning as well as maintain a solid command of the progress and practices of the data-driven academic library.

98bdf361a13cb0f0b797c8ad6a13f7c5?s=128

Sarah Tudesco

December 04, 2013
Tweet

Transcript

  1. What is a data-driven Academic Library?

  2. Data Driven – What does that mean? “Data driven means

    that progress in an activity is compelled by data, rather than by intuition of personal experience.” (Wikipedia) XKCD: HTTP://IMGS.XKCD.COM/COMICS/BOYFRIEND.PNG
  3. Process for Data Driven Organizations Question Plan Collect Analyze Recommend

  4. Identify your question Who is using the library?

  5. Develop a plan – Part 1 Refine the question •

    Who is coming to the library? Space • Who is checking out books? ILS • Who is using electronic resources? Web
  6. Develop a plan – Part 2 Look for data sources

    • Space: turnstile data, head-counts, room reservation systems • ILS: circulation reports • Web: web analytics, link resolver data, vendor usage reports
  7. Library Data Sources Systems Collections, Acquisitions Financials Circulation E-resources Resource

    Sharing Web Analytics Gate Counts Workflow Reference Bibliographic Instruction Cataloging/Metadata Preservation Patron Input Patron Surveys Focus Groups Social Media Analytics
  8. Collect Data If the data exists in a system, retrieve

    it in analyzable format. If the data doesn’t exist, begin to collect the data.
  9. Derive Insights • Analyze and visualize • Slice and dice

    the data in a variety of ways XKCD: HTTP://IMGS.XKCD.COM/COMICS/CORRELATION.PNG
  10. Circulation by Patron Group 0 10,000 20,000 30,000 40,000 50,000

    60,000 70,000 80,000 90,000 100,000 110,000 10,479 27,565 35,548 37,269 44,359 38,505 30,810 42,534 -12% FY14 29,715 12,315 FY13 76,737 34,048 37,307 FY12 87,585 37,118 FY11 91,359 39,751 FY10 102,793 42,604 FY09 99,457 39,895 FY08 89,641 35,221 FY07 76,936 31,644 FY06 85,089 33,070 Graduate Faculty Staff Other Carrel Resource Sharing Undergraduate Report run date: 11/25/2013
  11. Circulation by Patron Group (%) 39% 41% 39% 40% 41%

    44% 42% 44% 41% 10% 10% 8% 8% 7% 7% 7% 9% 9% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% FY09 FY07 FY08 FY10 FY11 FY12 FY13 FY14 FY06 35% 36% 41% 41% 43% 43% 43% 40% 44% Resource Sharing Carrel Other Staff Faculty Graduate Undergraduate Report run date: 11/25/2013
  12. Circulation - Undergraduates 0 5,000 10,000 15,000 20,000 25,000 30,000

    35,000 40,000 45,000 FY12 35,548 FY11 -22% FY14 10,479 FY13 27,565 FY07 30,810 FY06 37,307 FY09 42,534 FY08 38,505 37,269 FY10 44,359 3,534 24,805 13,700 18,692 12,118 18,489 9,076 22,773 14,534 23,382 12,166 24,504 12,765 29,413 14,946 28,348 14,186 6,945 Undergrad - Fresh, Soph, Junior Undergrad - Senior Report run date: 11/25/2013
  13. Library Circulation: Monthly Charges by Undergraduates - FY2011-FY2014 Comparison 0

    500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 Jul Aug Sep Oct Nov -35% Mar Apr May Jun -25% Feb Jan Dec 2013 2014 2012 2011
  14. Recommendations Use the insights derived from the data to make

    actionable recommendations. XKCD: HTTP://IMGS.XKCD.COM/COMICS/CONVINCING.PNG
  15. Skills • Data Capture • Programming & database skills •

    Business domain knowledge • Data Analysis • Spreadsheets • Reporting tools • Statistics • Presentation • Visualization • Storytelling
  16. Tools • Quantitative Analysis • Spreadsheets: Excel, Google Spreadsheets •

    Stats Tools: R, SATA, SPSS • Qualitative Analysis • ATLAS.ti • Survey Tools • Qualtrics, Webmonkey, Google Forms • Visualization • Tableau, Many Eyes
  17. Big Data – Gartner Hype Cycle 2012

  18. Thank You! Sarah Tudesco Assessment Librarian Yale University Library sarah.tudesco@yale.edu

    @studesco