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Book part
Publication date: 12 July 2021

Ryan Cheah Wei Jie, Cha Yao Tan, Fang Yenn Teo, Boon Hoe Goh and Yau Seng Mah

Big data have rapidly developed as a viable solution to many problems faced in engineering industries. Specifically, in the industry of water resource engineering, where…

Abstract

Big data have rapidly developed as a viable solution to many problems faced in engineering industries. Specifically, in the industry of water resource engineering, where there is a tremendous amount of data, various big data techniques could be applied to achieve innovative and efficient solutions for the industry. This study reviewed the proposal of big data as potential approaches to solve various difficulties encountered in managing water resources and related applications in Malaysia. The advantages and disadvantages of big data applications have also been discussed along with a brief literature review and some examples of case studies.

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Water Management and Sustainability in Asia
Type: Book
ISBN: 978-1-80071-114-3

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Article
Publication date: 14 February 2020

Nove E. Variant Anna and Endang Fitriyah Mannan

The purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's…

Abstract

Purpose

The purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation.

Design/methodology/approach

The Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019).

Findings

It can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well.

Originality/value

This paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library.

Details

Library Hi Tech News, vol. 37 no. 4
Type: Research Article
ISSN: 0741-9058

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Article
Publication date: 13 February 2017

David J. Pauleen and William Y.C. Wang

This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to…

Abstract

Purpose

This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information.

Design/methodology/approach

This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”.

Findings

A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations.

Research limitations/implications

This is an opinion piece, and the proposed model still needs to be empirically verified.

Practical implications

It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation.

Originality/value

The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

Details

Journal of Knowledge Management, vol. 21 no. 1
Type: Research Article
ISSN: 1367-3270

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Article
Publication date: 6 November 2017

Waqar Ahmed and Kanwal Ameen

The purpose of this paper is to define big data and draw its deep understanding. Moreover, trends of big data research in the field of library and information management…

Abstract

Purpose

The purpose of this paper is to define big data and draw its deep understanding. Moreover, trends of big data research in the field of library and information management are explored. With the purpose to explore the research trends, papers indexed in Thomson Reuters’ ISI Web of Knowledge were analyzed.

Design/methodology/approach

It is a literature-based and scientometric paper. A formal definition is constructed through a review of literature. Moreover, scientometric analysis of papers indexed in Thomson Reuters’ ISI Web of Knowledge has been done to explore the research trends associated with big data in the field of library and information science, using Vosviewer software.

Findings

The findings of the study indicate the reshaped definition of big data. The findings further indicate major research trends associated with big data. The analysis indicated “Risk”, “Industry”, “Market”, “Creditworthiness” and “Big Data Analytics”, the most repeated research trends associated with big data.

Practical implications

The paper sums up the learnings required to be a successful data-literate manager. First, the study defines big data. Second, the study describes current research trends associated with big data. Third, on the basis of the explored trends, data managers and library and information management professionals are guided about the learnings they require to be a successful data manager. Where thousands of data-literate managers are predicted to require in future, the present study is a guide to trends associated with big data.

Originality/value

It is a first study of its type which provides a reshaped definition of big data. It portrays its landscape and associated research trends in the field of information and library management (ILM).

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Article
Publication date: 23 July 2019

Emmanuel Sirimal Silva, Hossein Hassani and Dag Øivind Madsen

Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are…

Abstract

Purpose

Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are actively engaging with Big Data analytics to enhance their operations and maximise on profitability. In hope of motivating and providing direction to fashion retail managers, industry experts, and academics alike, the purpose of this paper is to consider the most recent and trending applications of Big Data in fashion retailing with the aim of concisely summarising the industry’s current position and status.

Design/methodology/approach

This conceptual paper provides a brief introduction to the emerging topic of Big Data in fashion retailing by briefly synthesising findings from industry, market and academic research.

Findings

Most existing fashion brands are yet to fully engage with Big Data. The authors find that the main reasons underlying the application of Big Data analytics in fashion are trend prediction, waste reduction, consumer experience, consumer engagement and marketing, better quality control, less counterfeits and shortening of supply chains. The authors also identify key challenges which must be overcome for the most fashionable industry to be able to capitalize on Big Data to understand and predict fashion consumer behaviour.

Research limitations/implications

The brief synthesis provides a foundation for future investigations into the use of Big Data in fashion retailing.

Originality/value

This paper serves as an up-to-date introduction to how Big Data can transform fashion retailing and can act as a sound reference guide for fashion industry managers and professionals grappling with Big Data-related issues.

Details

Journal of Business Strategy, vol. 41 no. 4
Type: Research Article
ISSN: 0275-6668

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Article
Publication date: 19 October 2015

Joseph Amankwah-Amoah

Although big data have emerged at the cornerstone of business and management research, past studies have failed to offer explanations and classifications of different…

Abstract

Purpose

Although big data have emerged at the cornerstone of business and management research, past studies have failed to offer explanations and classifications of different levels of capacity and expertise possessed by different countries in utilising big data. The purpose of this paper is to examine the different capacities of governments in utilising big data.

Design/methodology/approach

The paper is based on a comprehensive synopsis of the literature on big data and the role of governments in utilising and harnessing big data.

Findings

The study provides an array of explanations to account for why some countries are adept at using big data to solve social problems, while others often faltered.

Research limitations/implications

The study offers a range of explanations and suggestions, which include skills upgrading, to help countries improve their capabilities in data collection and data analysis.

Originality/value

In this paper, data collection-data analysis matrix was developed to characterise the role of governments in data collection and analysis.

Details

Industrial Management & Data Systems, vol. 115 no. 9
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 14 August 2018

Kar Hooi Tan

Although published research is limited to big data, some research focuses on the challenges that companies face in implementing big data projects. Specifically, in the…

Abstract

Purpose

Although published research is limited to big data, some research focuses on the challenges that companies face in implementing big data projects. Specifically, in the field of information systems, researchers realize that the success of Big Data projects is not only the result of data and analytics tools and processes, but also includes broader aspects. To address this issue, people have come up with a perception of big data analytics capabilities, often defined as the ability of businesses to take advantage of data management, infrastructure, and talent to turn business into competencies.

Design/methodology/approach

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings

The relationship between analytics and organizational performance has been the subject of the extant research. Prior studies have highlighted the direct influence of analytics on organizational performance. For example, big data analytics capabilities are significantly correlated with market performance and operational performance. The mechanisms through which analytics affect organizations were also examined from various perspectives.

Practical implications

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

Originality/value

The briefing saves busy executives 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.

Details

Strategic Direction, vol. 34 no. 8
Type: Research Article
ISSN: 0258-0543

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Article
Publication date: 9 September 2021

Jinlei Yang, Yuanjun Zhao, Chunjia Han, Yanghui Liu and Mu Yang

The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big

Abstract

Purpose

The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big challenge for the government to carry out financial market risk management in the big data era.

Design/methodology/approach

In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value.

Findings

Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike information criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored.

Originality/value

Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 18 August 2021

Chi Kwok and Ngai Keung Chan

This study aims to develop an interdisciplinary political theory of data justice by connecting three major political theories of the public good with empirical studies…

Abstract

Purpose

This study aims to develop an interdisciplinary political theory of data justice by connecting three major political theories of the public good with empirical studies about the functions of big data and offering normative principles for restricting and guiding the state’s data practices from a public good perspective.

Design/methodology/approach

Drawing on three major political theories of the public good – the market failure approach, the basic rights approach and the democratic approach – and critical data studies, this study synthesizes existing studies on the promises and perils of big data for public good purposes. The outcome is a conceptual paper that maps philosophical discussions about the conditions under which the state has a legitimate right to collect and use big data for public goods purposes.

Findings

This study argues that market failure, basic rights protection and deepening democracy can be normative grounds for justifying the state’s right to data collection and utilization, from the perspective of political theories of the public good. The state’s data practices, however, should be guided by three political principles, namely, the principle of transparency and accountability; the principle of fairness; and the principle of democratic legitimacy. The paper draws on empirical studies and practical examples to explicate these principles.

Originality/value

Bringing together normative political theory and critical data studies, this study contributes to a more philosophically rigorous understanding of how and why big data should be used for public good purposes while discussing the normative boundaries of such data practices.

Details

Journal of Information, Communication and Ethics in Society, vol. 19 no. 3
Type: Research Article
ISSN: 1477-996X

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Article
Publication date: 18 August 2021

Nastaran Hajiheydari, Mohammad Soltani Delgosha, Yichuan Wang and Hossein Olya

Big data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the…

Abstract

Purpose

Big data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to exploiting its essential value remains unclear. This study aims to provide a deeper understanding of the enablers facilitating BDA implementation in the banking and financial service sector from the perspective of interdependencies and interrelations.

Design/methodology/approach

We use an integrated approach that incorporates Delphi study, interpretive structural modelling (ISM) and fuzzy MICMAC methodology to identify the interactions among enablers that determine the success of BDA implementation. Our integrated approach utilizes experts' domain knowledge and gains a novel insight into the underlying causal relations associated with enablers, linguistic evaluation of the mutual impacts among variables and incorporating two innovative ways for visualizing the results.

Findings

Our findings highlight the key role of enabling factors, including technical and skilled workforce, financial support, infrastructure readiness and selecting appropriate big data technologies, that have significant driving impacts on other enablers in a hierarchical model. The results provide reliable, robust and easy to understand insights about the dynamics of BDA implementation in banking and financial service as a whole system while demonstrating potential influences of all interconnected influential factors.

Originality/value

This study explores the key enablers leading to successful BDA implementation in the banking and financial service sector. More importantly, it reveals the interrelationships of factors by calculating driving and dependence degrees. This exploration provides managers with a clear strategic path towards effective BDA implementation.

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

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