Connecting the Silos: Systematic Data Collection for Library and Collections Assessment

Karen R. Harker

Chapter 17 in Knowledge Discovery and Data Design Innovation:Proceedings of the International Conference on Knowledge Management (ICKM 2017), 2017, pp 319-333 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Assessment has gained greater prominence in library administration in the last decade. Whereas “library assessment” is associated with services, notably instructional or reference, collection assessment has evolved to its own specialty. The methods are largely quantitative and thus require extensive data sets, the management of which has required greater sophistication and technical expertise than in years past. Assessment, regardless of focus, is a data-intensive task. In order to make that judgment (of quality, meeting needs, educational attainment, etc.), evidence is needed. Bringing the data together in a manner that is effective and efficient has become a priority for organizations that need to assess on a regular basis. In this paper, I will describe the attempt we have made at The University of North Texas Libraries to organize data relevant for assessing our subject-based collections. This data are varied in formats, location, ownership, update frequency, and original purpose. I will present our most recent data model, our current methods of collecting, organizing, analyzing the data, and presenting results, as well as our plans for the future, which will include extending the reach of our connections.

Keywords: Knowledge Discovery; Big Data; Data Science; Data Analytics; Innovation (search for similar items in EconPapers)
JEL-codes: O30 (search for similar items in EconPapers)
Date: 2017
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