Using a Revised Knowledge Pyramid to Redefine Knowledge Management Strategy

2019 ◽  
pp. 1-18
Author(s):  
Murray E. Jennex
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Organizational Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Management Strategy ◽  
Big Data Analytics ◽  
Knowledge Management Strategy ◽  
Km Strategy ◽  
Customer Intelligence

The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom. This chapter posits that the knowledge pyramid is too basic and fails to represent reality and presents a revised knowledge-KM pyramid. One key difference is that the revised knowledge-KM pyramid includes knowledge management as an extraction of reality with a focus on organizational learning. The revised pyramid includes newer initiatives such as business and/or customer intelligence, big data, analytics, internet of things. Finally, this chapter discusses how KM strategy can be generated using the final revised pyramid.

Creating and Delivering a Successful Knowledge Management Strategy

2010 ◽  
pp. 261-276 ◽  
Author(s):  
Jiming Wu ◽  
Hongwei Du ◽  
Xun Li ◽  
Pengtao Li
Keyword(s):  
Knowledge Management ◽  
Organizational Learning ◽  
Competitive Advantage ◽  
Management Strategy ◽  
Management Support ◽  
Knowledge Management Strategy ◽  
Top Management Support ◽  
The Past ◽  
Km Strategy ◽  
Key Driver

Over the past decade, the rapid proliferation of knowledge management (KM) has been one of the most striking developments in business. Viewing KM as a key driver of competitive advantage, we attempt to provide managers with important guidance on how to create and deliver a successful KM strategy. Specifically, we develop a framework of three factors that are vital to KM success: top management support, a culture of organizational learning, and effective measures of KM performance. To offer a better understanding of the factors, their multiple facets are further investigated and discussed.

Developing a knowledge management strategy for data analytics and intellectual capital

2018 ◽  
Vol 26 (3) ◽  
pp. 400-419 ◽  
Author(s):  
Harold D. Harlow
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Intellectual Capital ◽  
Data Analytics ◽  
Management Strategy ◽  
Management Tool ◽  
Social Implications ◽  
Knowledge Management Strategy ◽  
Content Type ◽  
Strategic Intent

PurposeThis paper aims to build on current analytics and Big Data definitions and strategies from the literature to develop an overall strategic model connecting knowledge management strategy (KMS) for intellectual capital (IC) acquisition and business use. It also extends the IC research stages to a fifth stage of IC research including IC strategic intent.Design/methodology/approachA literature review highlights the connections among strategic intent, firm strategy, KMS and a data analytics strategy aligned with firm and KMS strategic intent. An extended model of the interrelationships is developed from the prior research.FindingsA model framework was developed from the literature that connects Big Data to achieve the goals of a firm KMS and demonstrates how Big Data analytics (BDA) needs to shift from being a tactical tool to a strategic knowledge management tool directed by the overall strategy and strategic intent of the firm.Research limitations/implicationsThe model presented needs to be empirically tested over a sample of companies and periods to determine if performance improves using this model.Practical implicationsUse of this model proposes that strategic intent will be enhanced and improve the capture of intellectual property derived from advanced analytics and increase sustainable advantages at firm.Social implicationsThe social implications of lack of strong privacy laws coupled with the possible elimination of millions of knowledge worker jobs creates a pressing need for more research into and identification of firm’s and government’s Big Data strategic use for both good and perhaps evil.Originality/valueThe research in this paper extends current models of IC development and adds strategic intent and collective intelligence as the fifth stage of IC research and presents an overall KMS/BDA model.

Big data analytics for 6G enabled massive Internet of Things

2021 ◽  
pp. 1-1
Author(s):  
Zhihan Lv ◽  
Ranran Lou ◽  
Jinhua Li ◽  
Amit Kumar Singh ◽  
Houbing Song
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals The Prospect of Internet of Things and Big Data Analytics in Transportation System

2018 ◽  
Vol 1018 ◽  
pp. 012013 ◽  
Author(s):  
Waleed Noori Hussein ◽  
L.M. Kamarudin ◽  
Haider N. Hussain ◽  
A. Zakaria ◽  
R Badlishah Ahmed ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Transportation System

RELATIONSHIP IN IOT AND BIG DATA ANALYTICS: THE OPPORTUNITIES AND OPEN CHALLENGES

2021 ◽  
Vol 83 (4) ◽  
pp. 100-111
Author(s):  
Ahmad Anwar Zainuddin ◽  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Physical World ◽  
Future Directions ◽  
Wide Range ◽  
Time Utilization ◽  
Relationship Of ◽  
The Internet Of Things

Internet of Things (IoT) is an up-and-coming technology that has a wide variety of applications. It empowers physical objects to be organized in a specialized framework to grow its convenience in terms of ease and time utilization. It is to convert the thought of bridging the crevice between the physical world and the machine world. It is also being use in the wide range of the technology in this current situation. One of its applications is to monitor and store data over time from numerous devices allows for easy analysis of the dataset. This analysis can then be the basis of decisions made on the same. In this study, the concept, architecture, and relationship of IoT and Big Data are described. Next, several use cases in IoT and big data in the research methodology are studied. The opportunities and open challenges which including the future directions are described. Furthermore, by proposing a new architecture for big data analytics in the Internet of Things, this paper adds value. Overall, the various types of big IoT data analytics, their methods, and associated big data mining technologies are discussed.

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