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This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

This chapter critically discusses implications of working with ‘big data’ from the perspective of qualitative research and methodology. A critique is developed of the analytic troubles that come with integrating qualitative methodologies with ‘big data’ analyses and, moreover, the ways in which qualitative traditions themselves offer a challenge, as well as contributions, to computational social science.

The chapter draws on Interactionist understandings of social organisation as an ongoing production, tied to and accomplished in the actual practices of actual people. This is a matter of analytic priority but also points to a distinctiveness of sociological work which may be undermined in moving from the study of such actualities, suggesting an alternative coming crisis of empirical sociology.

A cautionary tale is offered regarding the contribution and character of sociological analysis within the ‘digital turn’. It is suggested that ‘big data’ analyses of traces abstracted from actual people and their practices not only miss and distort the relation of social practice to social product but, consequentially, can take on an ideological character.

The chapter offers an original contribution to current discussions and debates surrounding ‘big data’ by developing enduring critiques of sociological methodology and analysis. It concludes by pointing to contributions and interventions that such an empirical programme of qualitative research might make in the context of the ‘digital turn’ and is of value to those working at the interface of traditional and digital(ised) inquiries and methods.

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

The purpose of this paper is to explore the data connection, spatial distribution characteristics and trends in genealogical information. First, it implements a spatial-temporal visualization of the Hakka genealogical information system that makes these individual family pedigree charts appear as one seamless genealogy to family and researchers seeking connections and family history all over the world. Second, this study applies migration analysis by applying big data technologies to Hakka genealogies to investigate the migration patterns of the Hakka ethnic group in Taiwan between 1954 and 2014. This innovative library service enhances the Hakka genealogical migration analysis using big data.

The platform is designed for the exchange of genealogical data to be used in big data analysis. This study integrates big data and geographic information systems (GIS) to map the population distribution themes. The general procedure included collecting genealogical big data, geographic encoding, gathering the map information, GIS layer integration and migration map production.

The analytical results demonstrate that big data technology is highly appropriate for family migration history analysis, given the increasing volume, velocity and variety of genealogical data. The spatial-temporal visualization of the genealogical research platform can follow family history and migration paths, and dynamically generate roadmaps to simplify the cartographic steps.

Technology that combines big data and GIS is suitable for performing migration analysis based on genealogy. A web-based application for spatial-temporal genealogical information also demonstrates the contribution of innovative library services.

Big data play a dominant role in library services, and in turn, provide an active library service. These findings indicate that big data technology can provide a suitable tool for improving library services.

Online genealogy and family trees are linked with large-volume, growing data sets that are complex and have multiple, autonomous sources. The migration analysis using big data has the potential to help genealogy researchers to construct minority ethnic history.

This study aims to provide a literature review and bibliometric analysis of sustainable supply chain management using big data. We reviewed the literature on sustainable supply chain management under big data from 2012 to 2019 and extracted 777 articles.

We conducted quantitative analysis and data network visualization of the chosen literature, including authors, journals, countries, research institutions and citations.

We discovered that the development of this interdisciplinary field has gained increasing popularity among researchers around the world, such as China and the US publishing the most articles and Western states having more cooperation, which indicates this research topic is growing in significance globally.

Scientific and technological revolutions such as big data have been incorporated in various industries. Modern supply chain management has also been combined with the advances in data science to achieve sustainability goals. No studies have reviewed the sustainable supply chain management based on big data. This study fills this gap.

The purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology, specifically big data analytics and the Internet of Things (IoT), have changed over time. The study also examines the ways in which research in supply chain and related fields differ when responding to and managing disruptive change.

This study follows a four-step systematic review process, consisting of literature collection, descriptive analysis, category selection and material evaluation. For the last stage of evaluating relevant issues and trends in the literature, the latent semantic analysis method was adopted using Leximancer, which allows more rapid, reliable and consistent content analysis.

The empirical analysis identified key research trends in big data analytics and IoT divided over two time-periods, in which research demonstrated steady growth by 2015 and the rapid growth was shown afterwards. The key finding of this review is that the main interest in recent big data is toward overlapping customer service, support and supply chain network, systems and performance. Major research themes in IoT moved from general supply chain and business information management to more specific context including supply chain design, model and performance.

In addition to providing more awareness of this research approach, the authors seek to identify important trends in disruptive technologies research over time.

The purpose of this paper is to explore the challenges posed by Big Data to current trends in computation, networking and storage technology at various stages of Big Data analysis. The work aims to bridge the gap between theory and practice, and highlight the areas of potential research.

The study employs a systematic and critical review of the relevant literature to explore the challenges posed by Big Data to hardware technology, and assess the worthiness of hardware technology at various stages of Big Data analysis. Online computer-databases were searched to identify the literature relevant to: Big Data requirements and challenges; and evolution and current trends of hardware technology.

The findings reveal that even though current hardware technology has not evolved with the motivation to support Big Data analysis, it significantly supports Big Data analysis at all stages. However, they also point toward some important shortcomings and challenges of current technology trends. These include: lack of intelligent Big Data sources; need for scalable real-time analysis capability; lack of support (in networks) for latency-bound applications; need for necessary augmentation (in network support) for peer-to-peer networks; and rethinking on cost-effective high-performance storage subsystem.

The study suggests that a lot of research is yet to be done in hardware technology, if full potential of Big Data is to be unlocked.

The study suggests that practitioners need to meticulously choose the hardware infrastructure for Big Data considering the limitations of technology.

This research arms industry, enterprises and organizations with the concise and comprehensive technical-knowledge about the capability of current hardware technology trends in solving Big Data problems. It also highlights the areas of potential research and immediate attention which researchers can exploit to explore new ideas and existing practices.

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same.

The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis.

Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes.

The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research.

This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes.

The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics.

While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.