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Library management and innovation in the Big Data Era

Document Cited in Related
Published date17 September 2018
DOIhttps://doi.org/10.1108/LHT-09-2018-272
Date17 September 2018
Pages374-377
AuthorShan Liu,Xiao-Liang Shen
Guest editorial
Library management and innovation in the Big Data Era
Introduction
Nowadays, big data has become a significant issue across different areas and received
considerable attention in both research and practice. Although scholars in library
and information science (LIS) discipline have claimed that big data presents a huge
opportunity for library research (Gordon-Murnane, 2012), the role of big data in
facilitating library management and innovation remains unclear and needs further
investigation. As witnessed, big datadriven library innovations provide personalized,
remote, real-time, and virtualized services to the library users. In addition, the rapid
increase in volume, veracity, velocity, and variety of library data generated by different
library tools offers innovative ways of understanding interactions with users in the
library environment (Nicholson and Bennett, 2016).
This special issue aims to address not only library management and innovation issues
exposed by library innovative applications, technology, and services, but also technical and
managerial approaches, methodologies, and solutions that would overcome the challenges
encountered by the librarians in the era of big data. This special issue makes some
significant and original contributions to both library management and LIS community in
general. For library practitioners and researchers, this special issue utilizes big data
thinking and data analytics approach to address the underlying management problems and
promote various innovations in the library. For LIS scholars, this special issue is an early
attempt to understand how library data can be acquired, preserved, processed, and applied
to generate considerable value and true insights.
A framework of big data-driven library management and innovation
Big data enables library to be smart and user-friendly by providing personalized and
intelligent services. Generally speaking, library big data can be categorized into two groups:
catalogue and process/transactional data. Catalogue data mean the inherent data and
information of library files, while process data are often generated through the process of
library management and service or created by library users. The former group of data
generally contains documental, bibliographical, and funding data, while the latter group
includes log, user, and record data. The analytics of library big data support tremendous
digital library innovations, such as personalized recommendation services and library user
behavior/habit analysis, which generate substantial value and insights for librarian, user,
and services. Values for librarian are manifested in the changes and benefits provided by
intelligent big data analytical techniques to librarians and digital management processes
that enables library to provide competitive products and services with minimal costs.
User value is embedded in the improvement of library user experience and satisfaction of
users. Service value includes improving service and process quality and efficiency with the
analysis of library big data in its varying forms.
Digital library innovation also requires library to provide a robust and intelligent digital
management system. Due to the high volume, variety, velocity, and veracity of big data, the
development of library management system requires not only the design of a new
architecture, but also the application of digital technology in managing library big data,
such as data acquirement, preservation, and processing. Therefore, both digital library
management and digital library innovation constitute a transition closed-loop system,
where library innovation drives library management, which in turn provides management
Library Hi Tech
Vol. 36 No. 3, 2018
pp. 374-377
© Emerald PublishingLimited
0737-8831
DOI 10.1108/LHT-09-2018-272
374
LHT
36,3

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