M. S. Sumbal, Murad Ali, U. Sahibzada, F. Nawaz, Adeel Tariq, H. Munir
{"title":"Big Data Based Knowledge Management vs. Traditional Knowledge Management: A People, Process and Technology Perspective","authors":"M. S. Sumbal, Murad Ali, U. Sahibzada, F. Nawaz, Adeel Tariq, H. Munir","doi":"10.6688/JISE.202109_37(5).0005","DOIUrl":null,"url":null,"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.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"208 1","pages":"1053-1065"},"PeriodicalIF":0.5000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Information Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.6688/JISE.202109_37(5).0005","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.