Purpose:
The twin rise in interest in library analytics (Showers, 2015) and UX (user experience) (Appleton, 2016) in libraries has led to new ideas and methods for studying the library and its users for evaluation and assessment. While librarians can and should acquire skills to employ techniques such as data mining and ethnography arguably working with non-librarians who have a different viewpoint can possibly yield fresh insights.
At the Singapore Management University (SMU), a University with a strong focus on experiential learning pedagogy and business intelligence (Gottipati & Shankararaman, 2016) collaboration with the library seems to be an obvious win-win strategy for both students who need to practice their skills in a real-world situation as well as researchers as a test-bed for new technologies.
The author who was hired as Library Analytics Manager in 2015, juggles a number of responsibilities including but not limited to ad hoc operational analysis, management of library business dashboard, library-student outcome correlation studies (Soria, Fransen, & Nackerud, 2013) as well as serves as a point person for collaborations on analytics within and outside the library (Tay, 2016). The collaborations that he has been involved in span a range of quantitative areas such as analytics and data mining on electronic resource usage and gate counts (with various student groups majoring in analytics), deploying new analytics using wifi tracking (with research centers), measuring value of information literacy (with writing centres) and in qualitative areas involving in-depth interviews and ethnographic studies (with students in research method classes). They also differ in complexity from collaborations with students in 101 classes on business performance improvement studies to multi-year collaborations with Ph.D. students and research analytics labs.
Design, methodology or approach:
The author proposes a model for academic libraries on managing collaborations in analytics and performance management with different participants based on level of expertise, purpose of collaboration and sensitivity of data.
Findings:
Analytics and performance assessment is a multi-faceted area, with many opportunities for collaboration. The challenge is with finding the best fit for collaboration that maximises the benefits for both the library and the collaborators.
Originality and value:
While collaborations with faculty on information literacy (Wijayasundara, 2008) are not new, collaborations on analytics is a new unexplored area and as great potential for synergy.
References:
Appleton, L. (2016). User experience (UX) in libraries: Let's get physical (and digital), Insights, 29(3), 224-227.
Gottipati, S. and Shankararaman, V. (2016). Designing a data warehousing and business analytics course using experiential learning pedagogy, Proceedings of SIGEd 2016 Conference.
Showers, B. (Ed.). (2015). Library Analytics and Metrics: Using data to drive decisions and services. Facet Publishing.
Soria, K. M., Fransen, J., & Nackerud, S. (2013). Library use and undergraduate student outcomes: New evidence for students' retention and academic success, portal: Libraries and the Academy, 13(2), 147-164.
Tay, A. (2016, November 18). 5 reasons why library analytics is on the rise [Web log post]. Retrieved from http://musingsaboutlibrarianship.blogspot.sg/2016/11/5-reasons-why-library-analytics-is-on.html
Wijayasundara, N. and Singh, D. (2008). An initiative to enhance the linking of information literacy in teaching through faculty-library collaboration, In Abrizah Abdullah, et al. (Eds.), Towards an information literate society: Proceedings of the International Conference on Libraries, Information and Society, 59-68.
Organisational issues , Staff , Culture , Analytics
