On How Big Data Revolutionizes Knowledge Management

2019 ◽  
pp. 39-60
Author(s):  
Asha Thomas ◽  
Meenu Chopra
Keyword(s):  
Big Data ◽  
Knowledge Management

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

Big data analytics capabilities and knowledge management: impact on firm performance

2019 ◽  
Vol 57 (8) ◽  
pp. 1923-1936 ◽  
Author(s):  
Alberto Ferraris ◽  
Alberto Mazzoleni ◽  
Alain Devalle ◽  
Jerome Couturier
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Structural Equation ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Integrated Approach ◽  
Content Type ◽  
Decision Making Processes ◽  
External Sources

Purpose Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues. Design/methodology/approach Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship. Findings The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities. Originality/value BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.

scholarly journals Advances in secure knowledge management in the big data era

2015 ◽  
Vol 17 (5) ◽  
pp. 983-986 ◽  
Author(s):  
Chittaranjan Hota ◽  
Shambhu Upadhyaya ◽  
Jamal Nazzal Al-Karaki
Keyword(s):  
Big Data ◽  
Knowledge Management

Our Knowledge Management Hubble May Need Glasses

2018 ◽  
Vol 14 (1) ◽  
pp. 30-50 ◽  
Author(s):  
William H. Money ◽  
Stephen J. Cohen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Real Time ◽  
Complex Data ◽  
Data Systems ◽  
Fault Handling ◽  
Architectural Framework ◽  
Big Data Systems ◽  
And Performance ◽  
Streaming Big Data

This article analyzes the properties of unknown faults in knowledge management and Big Data systems processing Big Data in real-time. These faults introduce risks and threaten the knowledge pyramid and decisions based on knowledge gleaned from volumes of complex data. The authors hypothesize that not yet encountered faults may require fault handling, an analytic model, and an architectural framework to assess and manage the faults and mitigate the risks of correlating or integrating otherwise uncorrelated Big Data, and to ensure the source pedigree, quality, set integrity, freshness, and validity of the data. New architectures, methods, and tools for handling and analyzing Big Data systems functioning in real-time will contribute to organizational knowledge and performance. System designs must mitigate faults resulting from real-time streaming processes while ensuring that variables such as synchronization, redundancy, and latency are addressed. This article concludes that with improved designs, real-time Big Data systems may continuously deliver the value of streaming Big Data.

Knowledge Management and Big Data in Business

2020 ◽  
pp. 37-51
Author(s):  
Jerzy Gołuchowski ◽  
Barbara Filipczyk
Keyword(s):  
Big Data ◽  
Knowledge Management

Knowledge Management and Business Analytics

2019 ◽  
pp. 1-42
Author(s):  
Nirali Nikhilkumar Honest ◽  
Atul Patel
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Business Performance ◽  
Virtual Organization ◽  
Data Driven ◽  
Business Analytics ◽  
Broad Category ◽  
Business Operations

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

Learning Management System 2.0

Web Services ◽  
2019 ◽  
pp. 2255-2270
Author(s):  
Muhammad Anshari ◽  
Yabit Alas ◽  
Norazmah Yunus ◽  
Norakmarul Ihsan binti Pg Hj Sabtu ◽  
Malai Hayati Sheikh Abdul Hamid ◽  
...  
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Big Data ◽  
Knowledge Management ◽  
Management System ◽  
Learning Management System ◽  
New Paradigm ◽  
Efficiency And Effectiveness ◽  
Main Driver ◽  
Learning Management

The recent adoption of cloud computing, Web 2.0 (web as a platform), and Big Data technologies have become the main driver of the paradigm shift. For higher education, choosing the right platform for a next generation of Learning Management System (LMS) namely LMS 2.0 is becoming more important than choosing a tool in the new paradigm. This chapter discusses factors for higher institution in determining a future direction for its LMS to take advantage of pervasive knowledge management, efficiency and effectiveness of operations. Literature studies have deployed for this study to portray the state of future LMS initiative. We found that the trends of cloud computing and big data will be predominant factor in viewing future LMS adoption and implementation. LMS 2.0 can be a solution to make learning systems in a higher education is flexible in terms of resources adoption, quality of learning, knowledge management, and implementation.

Big Data, Knowledge, and Business Intelligence

2018 ◽  
pp. 943-950
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Competitive Advantage ◽  
Intellectual Capital ◽  
Business Intelligence ◽  
Competitive Intelligence ◽  
Business Analytics ◽  
The Past ◽  
The People ◽  
Tools And Techniques

Knowledge management (KM), intellectual capital (IC), and competitive intelligence are distinct yet related fields that have endured and grown over the past two decades. KM and IC have always differentiated between the terms and concepts of data, information, knowledge, and wisdom/intelligence, suggesting value only comes from the more developed end of the range (knowledge and intelligence). But the advent of big data/business analytics has created new interest in the potential of data and information, by themselves, to create competitive advantage. This new attention provides opportunities for some exchange with more established theory. Big data gives direction for reinvigorating the more mature fields, providing new sources of inputs and new potential for analysis and use. Alternatively, big data/business analytics applications will undoubtedly run into common questions from KM/IC on appropriate tools and techniques for different environments, the best methods for handling the people issues of system adoption and use, and data/intelligence security.

Sign in / Sign up

Export Citation Format

Share Document