Big data and knowledge management: a case of déjà vu or back to the future?

2017 ◽  
Vol 21 (1) ◽  
pp. 113-131 ◽  
Author(s):  
Xuemei Tian
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Information Systems ◽  
Theory And Practice ◽  
Organizational Studies ◽  
Content Type ◽  
Online Databases ◽  
Management Perspective ◽  
Big Data Technologies ◽  
Conceptual Study

Purpose Big data clearly represent an important advance in information systems theory, but to describe it as “revolutionary” is premature. Similar technological breakthroughs, from online databases to ERP, were clearly modulated by advances in the organizational domain, including matters of structure, strategy and culture and arguably big data will be similar. The purpose of this paper is to encourage discussion of the wider implications of big data for the theory and practice of knowledge management. Design/methodology/approach This is a conceptual study based on critical analysis of the relevant literatures including those of organizational studies and management, big data and knowledge management. Findings The literature of big data emphasizes the application of algorithms to pattern analysis and prediction, resulting in data-driven decision-making, with data being the creator of value in organizations and societies. This would appear to render obsolete previous depictions of the “data-information-knowledge” relationship and, in effect, spell the end of knowledge management. However, big data literature largely ignores the organizational dimension and, significantly, the importance of frameworks, strategies and cultures for big data. As all of these are present in the literature of knowledge management, it would seem that big data have a long way to go to catch up and qualify even as a sub-discipline. Indeed, on the evidence, big data may well have a future as a contributor to and/or an element of knowledge management. Even for this to happen, however, major advances are required across the spectrum of big data technologies. Research limitations/implications This is a position paper written as the precursor for an empirical study. Originality/value The paper offers a critical literature-based and knowledge management perspective on big data while pointing out the common thread that runs through decades of advances in information systems technologies.

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.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

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.

Process architecture development using Riva and ARIS: comparative study

2018 ◽  
Vol 24 (3) ◽  
pp. 837-858 ◽  
Author(s):  
Dina Tbaishat
Keyword(s):  
Information Systems ◽  
Comparative Study ◽  
Academic Libraries ◽  
Theory And Practice ◽  
Organizational Processes ◽  
Content Type ◽  
Big Picture ◽  
Generic Library ◽  
The Right ◽  
Process Architecture

Purpose Business process modeling has been given great attention due to its crucial role in developing computer-based systems that support (and automate) organizational processes. In information systems, building the right process architecture is vital, since a poor division of organizational processes can lead to complex designs or incoherent structure. Moreover, process architecture acts as a “big picture” of what the organization does, and represents dynamic relationships between the existing processes, which, in turn, helps understand how the organization works (Ould, 2005). The paper aims to discuss these issues. Design/methodology/approach This paper discusses the derivation of a process architecture diagram (PAD) using the Riva method in detail, in contrast to the PAD developed using Architecture of Integrated Information Systems (ARIS). The information system selected as an example for this comparative study is in the context of academic libraries, embedding various yet generic library processes. Findings Organizational process architecture provides a basis for business management, based on specific framework supported by integrated tools and methods (Kozina, 2006). ARIS and Riva process architecture methods can be used to visualize and document organizational processes. Understanding the merits and weaknesses of each method is essential to identify possible areas of application. Research limitations/implications The processes selected were generic in most academic libraries not taking into consideration special personalized processes. These can be added though. Practical implications Both techniques are feasible and can be used to view and analyze library processes, allowing bridging the gap between theory and practice. Originality/value A number of process architecture methods are available, however, few studies focused on assessing these methods, and comparing some of them to find out how easy they are to be used in particular contexts, and whether they can be standardized.

Data, Information, and Knowledge

2015 ◽  
pp. 85-96
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Conceptual Development ◽  
Intangible Assets ◽  
Business Analytics ◽  
Management Perspective ◽  
Successful Employment ◽  
Similarities And Differences ◽  
Global Strategies ◽  
Cross Fertilization

This chapter examines the similarities and differences between big data and knowledge management. Big data has relatively little conceptual development, at least from a strategy and management perspective. Knowledge management has a lengthy literature and decades of practice but has always explicitly focused only on knowledge assets as opposed to precursors like data and information. Even so, there are considerable opportunities for cross-fertilization. Consequently, this chapter considers data from McKinsey Global Strategies on data holdings, by industry, and contrasts that with data on knowledge development, essentially the intangible assets found in the same industries. Using what we know about the variables influencing the application of intangible assets such as knowledge and intelligence, we can then better identify where successful employment of big data might take place. Further, we can identify specific variables with the potential to grant competitive advantage from the application of big data and business analytics.

Social Media and Big Data

2017 ◽  
pp. 315-332 ◽  
Author(s):  
Meena Jha ◽  
Sanjay Jha ◽  
Liam O'Brien
Keyword(s):  
Social Media ◽  
Big Data ◽  
Knowledge Management ◽  
Social Interaction ◽  
Missed Opportunities ◽  
Media Technologies ◽  
Big Data Technologies ◽  
Management Paradigms ◽  
Online Social Interaction

The introduction of new and the evolution of existing social media technologies have enabled efficient and broader communication through online social interaction. Today consumers' thinking has shifted towards their trusted network for guidance rather than simply accepting what organisations tell them. With the advent of social interaction, knowledge management paradigms are being stretched beyond their ability to deliver useful results, which is forcing change within organisations globally. Using only transactional and internal data will result in mistaken conclusions or missed opportunities. Social media helps organisations acquire and manage massive amounts of data to better understand their customers, products, competition, and markets and make better decisions using Big Data solutions. These solutions enable organisations to decide on the basis of evidence rather than intuition. This chapter introduces Big Data, Big Data technologies used for capturing knowledge from social media and discusses Big Data Solutions for organizations.

How the Internet of Things can help knowledge management: a case study from the automotive domain

2017 ◽  
Vol 21 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Lorna Uden ◽  
Wu He
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Internet Of Things ◽  
Current Knowledge ◽  
Time Data ◽  
Content Type ◽  
Useful Knowledge ◽  
Iot Devices ◽  
The Internet Of Things

Purpose Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT). Design/methodology/approach The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.

scholarly journals Use of information and communication technology to support employee-driven innovation in organizations: a knowledge management perspective

2014 ◽  
Vol 18 (4) ◽  
pp. 633-650 ◽  
Author(s):  
Leif Jarle Gressgård ◽  
Oscar Amundsen ◽  
Tone Merethe Aasen ◽  
Kåre Hansen
Keyword(s):  
Knowledge Management ◽  
Information And Communication Technologies ◽  
Organizational Structures ◽  
Content Type ◽  
Knowledge Resources ◽  
Organizational Conditions ◽  
Management Perspective ◽  
Private And Public ◽  
Information And Communication

Purpose – The purpose of this paper is to investigate how organizations focusing on employee-driven innovation (EDI) use information and communication technologies (ICT)-based tools in their innovation work. EDI involves systematic exploitation of knowledge resources in organizations. Thus, the role of ICT for efficient knowledge management is important in this respect. Design/methodology/approach – In-depth interviews with employees, managers and union representatives from 20 organizations focusing on EDI were conducted. The sample included organizations from eight different industries, representing both private and public sectors. Findings – The results show that ICT-based tools can support the processes of acquisition, dissemination and exploitation of knowledge, which are important aspects of EDI. However, use of ICT-based tools has to be aligned with organizational structures and professional role conduct to be efficient. Practical implications – This study contributes to practice by highlighting several factors that organizations should emphasize to succeed with application of external and internal knowledge in their innovation work. Originality/value – This study applies a knowledge management perspective on the role of ICT-based tools to support EDI in organizations. The findings contribute to an improved understanding of organizational conditions for succeeding with use of ICT-based tools in innovation work, and emphasize that perspectives on knowledge management, technology management and human resource management have to be combined to understand how EDI can be promoted by using ICT in organizations.

Knowledge management for construction organisations: a research agenda

Kybernetes ◽  
2018 ◽  
Vol 47 (9) ◽  
pp. 1778-1800 ◽  
Author(s):  
Vipula Sisirakumara Gunasekera ◽  
Siong-Choy Chong
Keyword(s):  
Knowledge Management ◽  
Construction Industry ◽  
Political Influence ◽  
Performance Outcomes ◽  
Project Performance ◽  
Project Planning ◽  
Theory And Practice ◽  
Content Type ◽  
Knowledge Conversion ◽  
Practical Implications

PurposeThis paper aims to review the knowledge management (KM) processes, knowledge conversion modes and critical success factors (CSFs) and contextualise them to the construction setting to guide effective KM implementation.Design/methodology/approachThis paper is conceptual in nature. It begins with a review of issues faced by construction organisations, which led them to consider implementing KM. This is followed by a comprehensive review of KM processes, knowledge conversion modes, KM CSFs and their application to the construction industry.FindingsBased on the socialisation, externalisation, combination and internalisation (SECI) model, the knowledge conversion modes are discussed, linking them to the KM processes of knowledge creation, sharing, storage and application. The KM CSFs identified from construction literature suggest that they can be categorised into two groups, namely, factors within organisational control (managerial influence, technological influence and resource influence) and factors beyond organisational control (social influence, political influence, environmental influence, economic influence, industry influence and construction technology influence). The resulting review is discussed in terms of how construction organisations can implement KM effectively to achieve the desired project performance outcomes in terms of time, cost and quality.Research limitations/implicationsAlthough this paper has made some theoretical contributions, a quantitative analysis will further reinforce its value both in theory and practice, particularly in terms of applying the KM processes and CSFs to different organisational, industry and country settings. A quantitative research is being carried out in the major construction sector in Sri Lanka to establish the relationships between the KM processes, knowledge conversion modes and KM CSFs with project performance outcomes, which will be reported in a subsequent publication.Practical implicationsAs the construction industry uses a considerable number of knowledge workers, implementing KM for project planning and execution is the key to sustaining the growth of construction organisations and industry, particularly when KM implementation is linked to project performance outcomes. Practical implications are provided in terms of what successful KM implementation entails.Social implicationsEffective KM implementation can serve as a conduit for construction organisations to build capacity and develop the ability to react quickly to social challenges brought about by different stakeholders, even before the project commences, so that the project performance outcomes will not be affected. Another social implication is the role played by project team members, in which efforts have to be put in place to facilitate the use of KM processes, so that teams can align project activities to the general good of their organisations.Originality/valueA comprehensive KM framework that guides the construction industry on KM implementation is long overdue. This research represents the first of such attempts to view KM from a wider perspective, both in terms of internal and external influences affecting construction organisations. Once the conceptual framework developed is validated, it is expected to bring enormous benefits to different stakeholders.

A knowledge management super-strategy fueled by big data

2019 ◽  
Vol 35 (1) ◽  
pp. 22-23
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Intellectual Capital ◽  
Management Strategy ◽  
Design Methodology ◽  
Business Value ◽  
Content Type ◽  
Pertinent Information ◽  
Conceptual Paper ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This conceptual paper proposes a model for growing company competitive advantage into the future by integrating a knowledge management strategy with progressive insights from Big Data and artificial intelligence. The ultimate strategic aim here is to create and codify intellectual capital that adds business value. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

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