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Customer Focused Collection Services in the Age of Big Data

Received: 29 September 2017     Accepted: 19 October 2017     Published: 4 April 2018
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Abstract

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.

Published in International Journal of Intelligent Information Systems (Volume 7, Issue 1)
DOI 10.11648/j.ijiis.20180701.12
Page(s) 5-8
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Collection Services, Big Data, Academic Libraries, Demand Driven Acquisitions, Assessment, Digital Collections, Discovery Services, Customer Focused, Library and Information Science

References
[1] 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.
[2] 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/
[3] Evans, G. E., Intner, S. S., & Weihs, J. (2002). Introduction to Technical Services. Greenwood Village, CO: Libraries Unlimited.
[4] 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
[5] Gorman, G. E., & Miller, R. H. (1997). Collection management for the 21st century: a handbook for librarians. Westwood, CT: Greenwood Press.
[6] Johnson, P. (2009). Fundamentals of collection development and management. Chicago, IL: American Library Association.
[7] 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
[8] Magrill, R. M., & Corbin, J. (1984). Acquisitions management and collection development in libraries. Chicago, IL: American Library Association.
[9] 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.
[10] SUSHI FAQ: General Questions. (2010, July 8). Retrieved from NISO: http://www.niso.org/workrooms/sushi/faq/general
[11] Swords, D. A. (2011). Patron-driven acquisitions: history and best practices. Boston: De Gruyter Saur.
[12] What is Big Data? (2016). Retrieved from SAS: http://www.sas.com/en_us/insights/big-data/what-is-big-data.html
[13] 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
Cite This Article
  • APA Style

    Ying Zhang. (2018). Customer Focused Collection Services in the Age of Big Data. International Journal of Intelligent Information Systems, 7(1), 5-8. https://doi.org/10.11648/j.ijiis.20180701.12

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    ACS Style

    Ying Zhang. Customer Focused Collection Services in the Age of Big Data. Int. J. Intell. Inf. Syst. 2018, 7(1), 5-8. doi: 10.11648/j.ijiis.20180701.12

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    AMA Style

    Ying Zhang. Customer Focused Collection Services in the Age of Big Data. Int J Intell Inf Syst. 2018;7(1):5-8. doi: 10.11648/j.ijiis.20180701.12

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  • @article{10.11648/j.ijiis.20180701.12,
      author = {Ying Zhang},
      title = {Customer Focused Collection Services in the Age of Big Data},
      journal = {International Journal of Intelligent Information Systems},
      volume = {7},
      number = {1},
      pages = {5-8},
      doi = {10.11648/j.ijiis.20180701.12},
      url = {https://doi.org/10.11648/j.ijiis.20180701.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20180701.12},
      abstract = {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.},
     year = {2018}
    }
    

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    AB  - 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.
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Author Information
  • University of Central Florida Libraries, Orlando, USA

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