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作 者:叶英平 陈海涛[1] 陈皓[1] Ye Yingping;Chen Haitao;Chen Hao(Management School of Jilin University,Changchun 130022)
机构地区:[1]吉林大学管理学院
出 处:《图书情报工作》2019年第5期5-13,共9页Library and Information Service
基 金:中国科学技术协会高端科技创新智库青年项目“产学合作中网络权力、网络惯例对企业创新能力的影响机制研究”(项目编号:DXB-ZKQN-2017-030);中国博士后科学基金面上资助项目“创新联盟中网络惯例的演进及对联盟绩效的影响机制研究”(项目编号:2018M641789)研究成果之一
摘 要:[目的/意义]大数据时代给传统知识管理带来了变革,为适应大数据时代的到来,需要重新认识和定位知识管理过程,应用大数据技术工具构建新的知识管理模型,以应对知识管理的更新与挑战。[方法/过程]通过梳理数据到知识的形成过程,结合大数据的4V特征,了解知识管理所需的大数据技术工具,并将大数据时代的知识管理划分为知识生产、知识积累、知识交流、知识应用4个阶段,结合大数据技术工具的使用,构建新的知识管理模型,提出应对碎片整合、应用价值、硬件支撑、隐私伦理等问题的对策。[结果/结论]大数据的发展推动了知识管理过程从传统模型向技术模型的转型,大数据时代的知识管理模型与大数据技术的使用紧密相关,强调从海量碎片化数据中提炼知识价值,并更有效地辅助组织决策,为此,需要做好硬件设施支撑和信息安全保障,将大数据技术与小样本分析相结合,推动知识管理走向新的层次。[Purpose/significance] The period of big data has brought changes to traditional knowledge management.In order to adapt to the coming of the big data,it is necessary to re-understand the process of knowledge management,and use technology tools to build new knowledge management models to overcome the challenge of knowledge management.[Method/process] After the summarization of the formation process of data to knowledge,together with 4V features of big data,and the analysis of big data technology tools needed for knowledge management,the knowledge management in the period of big data is divided into four stages: knowledge production,knowledge accumulation,knowledge exchange and knowledge application.With the help of technology tools,a new model of knowledge management is built.Solutions are proposed to the problems such as debris consolidation,application value,hardware support,and ethics on privacy.[Result/conclusion] Big data promote the development of the transformation of the knowledge management process from the traditional model.The model of knowledge management in the period of big data is closely related to the use of technology tools.It emphasizes the value of knowledge extracted from mass fragmentation data and more effectively assists organizational decision making.Therefore,hardware support,information security,and combining big data technology with small sample analysis will push knowledge management to a new level.
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