Big Data Based Knowledge Management vs. Traditional Knowledge Management: A People, Process and Technology Perspective

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
M. S. Sumbal, Murad Ali, U. Sahibzada, F. Nawaz, Adeel Tariq, H. Munir
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引用次数: 4

Abstract

Value creation is one of the core aspects of Big Data. This concept of value creation can be linked to the efficient knowledge management within the organizations, in terms of knowledge creation, sharing and application, through which organizations can enhance their organizational performance. Little work has been done on the linkage of value creation from big data and the knowledge management capability of the organizations in terms of people, processes and technology which play a crucial role in effective knowledge management. This study contributes towards the existing body of knowledge by exploring this linkage of people, process and technology in relation to big data through the lens of knowledge management, by conducting a qualitative study in the oil and gas industry. The findings reveal that the KM capability of the organizations through big data can be explained through the Complex domain of Cynefin framework which involves probing, sensing and responding in which there are no right answers and instructive patterns (predictive knowledge) emerging from big data could be right or wrong depending upon the complexity of the situation. The useful and tested predictive knowledge by experts (people) can then emerge as good or best practice falling into complicated and simple domains of Cynefin framework.
基于大数据的知识管理与传统知识管理:人、流程和技术视角
价值创造是大数据的核心内容之一。这种价值创造的概念可以与组织内部有效的知识管理联系起来,在知识的创造、共享和应用方面,组织可以通过知识创造、共享和应用来提高组织绩效。大数据的价值创造与组织在人员、流程和技术方面的知识管理能力之间的联系,在有效的知识管理中起着至关重要的作用,这方面的研究很少。本研究通过对油气行业进行定性研究,从知识管理的角度探讨了与大数据相关的人员、流程和技术之间的联系,为现有知识体系做出了贡献。研究结果表明,组织通过大数据的知识管理能力可以通过Cynefin框架的复杂域来解释,该框架涉及探测、感知和响应,其中没有正确的答案,而从大数据中出现的指导性模式(预测性知识)可能是正确的,也可能是错误的,这取决于情况的复杂性。专家(人)的有用的和经过测试的预测知识可以成为Cynefin框架复杂和简单领域的良好或最佳实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Information Science and Engineering
Journal of Information Science and Engineering 工程技术-计算机:信息系统
CiteScore
2.00
自引率
0.00%
发文量
4
审稿时长
8 months
期刊介绍: The Journal of Information Science and Engineering is dedicated to the dissemination of information on computer science, computer engineering, and computer systems. This journal encourages articles on original research in the areas of computer hardware, software, man-machine interface, theory and applications. tutorial papers in the above-mentioned areas, and state-of-the-art papers on various aspects of computer systems and applications.
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