Customer Focused Collection Services in the Age of Big Data
International Journal of Intelligent Information Systems
Volume 7, Issue 1, February 2018, Pages: 5-8
Received: Sep. 29, 2017;
Accepted: Oct. 19, 2017;
Published: Apr. 4, 2018
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Ying Zhang, University of Central Florida Libraries, Orlando, USA
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As part of library core functions, collection services had always focused on resources and processes in the print age. With the advent of big data with prevailing digital technologies in the recent decades, academic libraries in the U.S. have increasingly brought customer into the center of collection services. Big data empower these customer-focused services in various formats and scopes. What are some common practices? How effective are they in addressing the customer needs while fulfilling the conventional goals of collection services? This article starts with a historical overview on the evolutions of collection activities from the perspectives of academic libraries in the U.S. It then shares several key trends and common practices enabled by big data to build collection services centering on customers, including demand driven acquisitions models, digital collections development, collection access and discovery enhancements and systematic collection assessments. The article also discusses the multitudes of implications and impacts brought by these new customer-focused collection services on the library and information science (LIS) profession, in technologies, in philosophies, in personnel, in budgets and certainly in user experience.
Collection Services, Big Data, Academic Libraries, Demand Driven Acquisitions, Assessment, Digital Collections, Discovery Services, Customer Focused, Library and Information Science
To cite this article
Customer Focused Collection Services in the Age of Big Data, International Journal of Intelligent Information Systems.
Vol. 7, No. 1,
2018, pp. 5-8.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Breeding, M. (2015). The Future of Library Resource Discovery: A white paper commissioned by the NISO Discovery to Delivery (D2D) Topic Committee. Baltimore, MD: NISO.
Elliott, T. (2015, March 23). Big Data Discovery Is The Next Big Trend In Analytics. Retrieved from ZDnet: http://www.zdnet.com/article/big-data-discovery-is-the-next-big-trend-in-analytics/
Evans, G. E., Intner, S. S., & Weihs, J. (2002). Introduction to Technical Services. Greenwood Village, CO: Libraries Unlimited.
Goodwin, G. (2014, February 18). Leading Vs. Lagging Indicators and the Coming LoT-Big Data Explosion. Retrieved from LNS Research: http://blog.lnsresearch.com/blog/bid/194257/Leading-Vs-Lagging-Indicators-and-the-Coming-IoT-Big-Data-Explosion
Gorman, G. E., & Miller, R. H. (1997). Collection management for the 21st century: a handbook for librarians. Westwood, CT: Greenwood Press.
Johnson, P. (2009). Fundamentals of collection development and management. Chicago, IL: American Library Association.
Kanellos, M. (2016, March 3). 152,000 Smart Devices Every Minute In 2025: IDC Outlines The Future of Smart Things. Retrieved from Forbes: http://www.forbes.com/sites/michaelkanellos/2016/03/03/152000-smart-devices-every-minute-in-2025-idc-outlines-the-future-of-smart-things/#3439e47669a7
Magrill, R. M., & Corbin, J. (1984). Acquisitions management and collection development in libraries. Chicago, IL: American Library Association.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition. Washington, DC: McKinsey Global Institute.
SUSHI FAQ: General Questions. (2010, July 8). Retrieved from NISO: http://www.niso.org/workrooms/sushi/faq/general
Swords, D. A. (2011). Patron-driven acquisitions: history and best practices. Boston: De Gruyter Saur.
What is Big Data? (2016). Retrieved from SAS: http://www.sas.com/en_us/insights/big-data/what-is-big-data.html
Zhang, Y. (2014, December 9). Evidence Based Acquisitions: does the evidence support this hybrid model? Retrieved from Information Today: http://www.infotoday.eu/Articles/Editorial/Featured-Articles/Evidence-Based-Acquisitions-does-the-evidence-support-this-hybrid-model-101019.aspx