IT-Architecture for Corporate Knowledge Management Systems

Zhi-Qin Liu, Aleksandr Deryagin, Sergey Glushkov

Abstract


For the efficient implementation of the corporate knowledge management sys-tems, common elements of their IT architecture, functional role as well as other specific features of their use are to be understood. The objective of the research is to identify common elements of IT architecture in the knowledge management systems in the corporate segment. To identify the common elements of the IT ar-chitecture in the knowledge management systems we have used the method of taxonomic classification of knowledge areas with a complex structure based on the example of a comparative analysis of various software products for corporate training, as well as case studies of this issue using the example of enterprises and organizations in China. We have used data from surveys of employees and com-panies as regards the development prospects of the corporate knowledge man-agement systems. The sample scope is 1000 managers of the companies from Eu-rope, the Middle East, Japan and China. For the taxonomic analysis 42 corporate knowledge management systems have been selected, which are used in training and represented in the world market. The integration of new technologies into business processes has caused the demand for new knowledge management sys-tems. Due to analysis results of 42 corporate knowledge management systems for learning, which are represented in the market, we can state that the majority of them have been developed on the grounds of the use of cloud technologies. In the total structure its share makes up almost 83 %, whereas 17 % refers to the rest of the corporate knowledge management systems for learning, as well as their com-bination. The use of obtained research results in practice is supposed if strategic approaches of the implementation of the corporate knowledge management sys-tems at enterprises of China and other countries are justified.

Keywords


data storage; enterprise; learning management system; taxonomy.

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Copyright (c) 2020 Zhi-Qin Liu, Aleksandr Deryagin, Sergey Glushkov


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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