朝乐门.信息资源管理理论的继承与创新:大数据与数据科学视角[J].中国图书馆学报,2019,45(2):26~42
Developing Information Resources Management Studies: Big Data and Data Science Perspectives
信息资源管理理论的继承与创新:大数据与数据科学视角
Received:September 21, 2018  Revised:February 14, 2019
DOI:
Key words:Information resources management  Big data  Data science
中文关键词:  信息资源管理  大数据  数据科学
基金项目:本文系中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目“基于数据科学的信息资源管理研究的继承与创新”(编号:18XNB012)的研究成果之一
Author NameAffiliationE-mail
CHAO Lemen 中国人民大学信息资源管理学院 北京 100872 chaolemen@ruc.edu.cn,chaolemen@ruc.edu.cn 
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Abstract:
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 thenext 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 plans driven by information resources, information resource centered organizational governance. 2)information resource ensuring methods,such as information resource planning, capability maturity models on IRM as well as digital continuity assurance. 3)deep wrangling of information resources,including tidying information resources, intelligent IRM technologies and human machine collaborative information resource processing methods.4)prescriptive analysis of information resources, especially introducing simulation, real time analysis, visualization technologies into IRM. 5)on time insights on information resources,including information resource awareness and literacy, in time services on information request, and information resource centered computing pattern. 6)product development on information resources,such as embedding applications of information resources, converting to information resources into business process, information resource driven services, service experience. 7)asset management of information resource,including digital humanities and digital economy, market oriented development and industrialization of information resources, information resource ecosystems. 4 figs. 2 tabs. 42 refs.
中文摘要:
      在大数据时代,信息资源管理理论研究面临着新的挑战与重要变革。本文首先分析大数据时代信息资源管理研究面临的四个主要挑战:研究假定有待重新审视,研究范围亟待进一步拓展,研究范式需要多样化,主要应用场景已发生变迁;其次,探讨了数据科学的研究进展以及数据科学与信息资源管理研究的内在关联;接着,提出了下一代信息资源管理理论的主要变革:从信息资源的管理到基于信息资源的管理,从模式在先到模式在后或无模式,从目标/任务驱动型管理到数据驱动型管理,从计算密集型应用到数据密集型应用,从基于知识的研究范式到基于数据的研究范式;最后,提出了下一代信息资源管理的主要研究问题:基于信息资源的管理与治理、信息资源的保障方法、信息资源的深度加工、信息资源的规范分析、信息资源的快速洞见、信息资源的产品化研发、信息资源的资产化管理。图4。表2。参考文献42。
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