and big data/analytics, of which there is currently almost no conceptual or empirical research. Specifically, it addresses the question: ⢠In what ways can KM ...
Does Big Data mean Big Knowledge? KM Perspectives on Big Data and Analytics Special issue call for papers from Journal of Knowledge Management
What is the special issue about? Guest edited by David J Pauleen, Massey University, Auckland and William Yu Chung Wang, Auckland University of Technology Auckland, New Zealand, this special issue focuses on KM and big data/analytics, of which there is currently almost no conceptual or empirical research. Specifically, it addresses the question: • In what ways can KM conceptually and in practice act as an intermediary between big data/analytics and management decision-making?
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Specific topics we invite you to provide submissions on: We are seeking both conceptual and empirical papers offering new insights into the following topics: • Techniques, methods and models in accessing Big Data as part of KMS • Case studies of utilizing Big Data in KMS and KM initiatives • How filtered Big Data results can be used continuously as organizational knowledge • Big Data and its implications for personal knowledge management. • Does Big Data mean big knowledge? Critical perspectives are welcome. • Identifying the gaps between traditional data and information approaches and the opportunities in Big Data. • Market Analysis – Systems vendors and organizational users. • New organizational Big Data and analytics initiatives as a part of KM strategy and practice. • Trends in Big Data - eCommerce, MCommerce and Ubiquitous Commerce, how they contribute to Big Data - and KM models for business application and management decision making. • Technology impacts – Internet of Things and Big Data, what’s next for Knowledge Management? • Analytical ability and insight: what are the implications for organizational staffing and professionals?
Proposed schedule: Submission deadline: July 31, 2015 Papers reviewed: October 31, 2015 Revised papers reviewed and accepted: January 31, 2016 Final versions of accepted papers delivered: February 30, 2016
Further guidance on submission: • • • •
Please read the publication style guidelines before submitting your paper Go here to register/or log on to submit your paper to ScholarOne Papers should be 7,000 to 10,000 words in length and not published previously in any other journal (print or electronic format) If you have any question about the special issue or your intended submission, please contact the guest editors.
More information about the subject of the special issue: In the Era of Exabytes (e.g., Google Datacenter is estimated to contain 15 exabytes [Munroe,
2014]), information on the world-wide internet has become too large and complex for traditional tools to analyze. Big data is the term used to describe this phenomenon. Big data can be understood primarily as the huge amounts of data generated by the Internet, mobile phones, tablets, etc. and characterized by the 3 V’s, Volume, Velocity, and Variety (Laney, D., 2001; McAffey & Brynjolfsson, 2012; O’Leary, 2013; Watson & Marjanovic, 2013) Research interested in big data has been growing quickly: Sixteen published articles/editorials in 2011 with “Big Data” in their title or topic with the numbers rising to 60 in 2012, and to 217 in 2013 (Web of Science). Organizational interest and awareness of big data is also increasing as demonstrated by articles in practitioner journals and the popular press; however, not all practitioners are well-informed on the opportunities, limitations and challenges presented by big data and throughout management knowledge gaps can be identified (Davenport, 2013; LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011; Watson & Marjanovic, 2013). Business analytics are “the techniques, technologies, systems, practices, methodologies, and applications”(Chen et al., 2012: 1166) that analyze big data. Analytics can help organizations to better understand the business and social environment and improve real and near real time decision making. Big data analytics, data mining and analysis, now include text, Web, network, and mobile analytics. The reach of big data and analytic knowledge is impressive and pervasive. Diverse areas such as e-commerce, market intelligence, e-government, science and technology, health and wellbeing, and security and public safety are all making use of big data and innovative analytics (Chen et al., 2012). However, big data is complex and so is the analysis of it. Extracting valuable knowledge from big data might only be possible for a limited number of highly trained personnel. Can such knowledge be made available organization wide, so everybody can benefit from it? Big data and analytics present both opportunities and challenges in many areas of business and government. This special issue is concerned with how Knowledge Management (KM) can help address these challenges. For more than two decades KM has addressed issues of information and knowledge management in organizations and in particular in relationship to organizational decision-making. As such, KM and Knowledge Management Systems (KMS) can offer both tools and conceptual understanding in the management and use of big data and analytics in organizational management and decision-making.
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Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4). Davenport, T. H. (2013). Analytics 3.0. Harvard Business Review, 91(12), 64-72. Google's Data Centers on Punched Cards". Randall Munroe. October 4, 2013. Retrieved May 10, 2014. Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. Retrieved from blogs.gartner.com/doug-laney/ LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data, Analytics and the Path from Insights to Value. MIT Sloan Management Review, 21. McAfee, A., & Brynjolfsson, E. (2012). Big Data: the Management Revolution. Harvard Business Review, 90(10), 60-66. O'Leary, D. E. (2013). Artificial Intelligence and Big Data. IEEE Intelligent Systems, 28(2), 0096-0099. Pauleen, D. & Gorman, G. (eds.) (2011) Personal Knowledge Management: Individual, Organizational and Social Perspectives. Gower:London. Pauleen, D. (Ed) (2009). “Personal Knowledge Management: Putting the ‘person’ back into the knowledge equation" Special Issue on Personal Knowledge Management. Online Information Review, 33 (2), 221-224. Pauleen, D. (Ed) (2006) “Cross-Cultural Perspectives on Knowledge Management. Libraries Unlimited: Watson, H. J., & Marjanovic, O. (2013). Big Data: The Fourth Data Management Generation. Business Intelligence Journal, 18(3), 4-8.
More about the guest editors: • David J Pauleen is an Associate Professor in Massey University, Auckland. He holds a PhD from the University of Victoria, Wellington. He has published a number of articles on knowledge management and has (co-) edited two books (2006, 2011) on knowledge management and a special journal issue (2009) on personal knowledge management. • Associate Professor William Y C Wang is currently working for the Auckland University of Technology, Auckland. He has served on the editorial board/advisory board for a number of international journals. His publications are related to the management of large scale systems including enterprise systems, supply chain management, and associated business intelligence. - See more at: http://emeraldgrouppublishing.com/products/journals/call_for_papers.htm?id=5855#sthash.uc u8NySD.dpuf
