Page 147 - Journal of Library Science in China, Vol.45, 2019

146
146 Journal of Library Science in China, Vol.11, 2019

Developing information resources management studies: Big Data and data
science perspectives
CHAO Lemen 〇a ∗

Information Resource Management (IRM) faces four major challenges in the big data era. First,
its research hypothesis needs to be re-examined: information resource is enriched or not yet?
What are the main concerns of information resource-enriched era? Information resource studies
should put more value on data-intensive problems or compute-intensive ones? The answers to
those questions are crucial to the evolution of IRM studies. Second, next-generation IRM needs
to expand its research scope and focus on information resource-based management issues. Third,
there is also a need to shift its research paradigm and embrace the data paradigm that makes use
of data to solve problems, prior to converting data into knowledge. Finally, the main use cases
have been changed. The common use cases of IRM theory have been changed in that information
is regarded as not only crucial resources but also key assets to modern organizations. As a result,
next-generation IRM studies have to conduct in-depth studies on information resources from an
asset perspective.
The main shifts of the next-generation IRM studies are: 1) From the management of information
resource to information resource-based management. Unlike the management of information
resource, information resources-based management put more concerns on how to design and
optimize business processes or decision making based on information resources and how to ensure
them be driven by data or information resources instead of the leader’s willingness. Information
resource-based management can enhance the agility and the flexibility of organizational
management and decision-making activities. 2) From schema first to schema later or never IRM:
schema of information resources is constantly changing, does not exist at all or has to manage
information resources before their schema has been identified yet in modern business environments.
3) From target/task-driven management to data-driven management: accelerating the process of
converting information resources to material resources and energy resources always needs a real
time computing. Further, the key to implement real time management of information resources is to
enable the management to be driven by data or information resources. 4) From compute-intensive
applications to data-intensive applications: the research problems of IRM shift from compute-
intensive use cases to data-intensive ones, and the main challenges come from data instead of
computing. 5) From the knowledge-based scientific paradigm to the rise of the data-based scientific
paradigm. One of the emerging topics of IRM is how to address the practical problems via the data
paradigm without fully grasping the knowledge and experience of specific fields.
Some emerging topics in the next-generation IRM is also proposed: 1) information resource-
based management and governance, including data-intensive scientific discovery, real-time
monitoring and dynamic optimization of information resources, security policies and emergency
* Correspondence should be addressed to CHAO Lemen, Email: chaolemen@ruc.edu.cn, ORCID: 0000-0001-8963-7507