Big Data Analytics: A Field of Opportunities for Information Systems and Technology Researchers
30 Nov 2016-Journal of Global Information Technology Management (Routledge)-Vol. 19, Iss: 4, pp 217-222
TL;DR: In this article, some of the critical aspects of the big data problem are discussed, and their importance as well as relevance to information systems research is shown.
Abstract: In recent years, the information systems research community has been experiencing an exciting time discovering and coming to grips with many unexplored or untapped opportunities for research that lie at the intersection of the disciplines of information systems and technology, big data, analytics, and data science. In this article, some of the critical aspects of the big data problem are discussed, and their importance as well as relevance to information systems research is shown. Also proposed are a number of research problems in this area that interested information systems scholars can address to delve more deeply into the field as well as to generate further active interest of the subject within the information systems community.
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TL;DR: Citation and co-citation analysis reveals that there is cross-functional nature of big data research, which permeates different business sectors and is influenced by themes in engineering and information management.
Abstract: In this study, big data studies (01/2015–6/2018) are reviewed and several highly cited papers are identified, which indicates a growing interest in the area of big data. The papers and proceedings from international peer-reviewed journals and ranked conferences were reviewed. We employed Principal component analysis and citation and co-citation analysis to identify themes of research emanating from these studies. Citation and co-citation analysis reveals that there is cross-functional nature of big data research, which permeates different business sectors and is influenced by themes in engineering and information management.
18 citations
Cites background from "Big Data Analytics: A Field of Oppo..."
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TL;DR: In this article, the authors explore organizational and human factor-related challenges to information technology (IT) service management standard ISO 20000 in an emerging economy context and propose some implications of the challenges to implementing environmental sustainability and circular economy.
Abstract: The purpose of this paper is to explore organizational and human factor-related challenges to information technology (IT) service management standard ISO 20000 in an emerging economy context. Then, this research has proposed some implications of the challenges to implementing environmental sustainability and circular economy.,To fulfill the research purpose, an empirical study was undertaken. The data required for the current study, based on a Likert scale and using questionnaires, were collected through surveys, interviews, telephonic conversations and meetings with IT firm managers and staff. The ranking of challenges was obtained based on the mean and standard deviation calculated from the survey responses.,The results indicated that senior management support was the most significant challenge for the successful implementation of IT Service Management systems. Other significant challenges were the justification of significant investment, premium customer support, co-operation and co-ordination among IT support teams, proper documentation and effective process design.,The current research is expected to help IT managers implement ISO 20000 and to manage environmental sustainability and circular economy across their organizational networks.,To the best of the authors’ knowledge, the current study is the first attempt to explore the organizational and human factor-related challenges to ISO 2000 in an emerging economy context. Furthermore, the current study proposes implications to the challenges to environmental sustainability and circular economy.
7 citations
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TL;DR: Content analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the library and information science (LIS) perspective.
Abstract: The purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective.,Content analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the LIS perspective.,A content analysis of 80 publications is presented. The articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. The category of tools, techniques and applications of data science was most addressed by the authors, followed by data science from the perspective of health sciences, data science education and training and knowledge and skills of the data professional. However, several publications fell into several categories because these topics were closely related.,Only publication recorded in the Web of Science database and with the term “data science” in the topic area were analyzed. Therefore, several relevant studies are not discussed in this paper that either were related to other keywords such as “e-science”, “e-research”, “data service”, “data curation”, “research data management” or “scientific data management” or were not present in the Web of Science database.,The paper provides the first exploration by content analysis of the field of data science from the perspective of the LIS.
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01 Jan 2019
TL;DR: Analyzing Small Businesses’ Adoption of Big Data Security Analytics by Henry Mathias MCA, Anna University, 1995 BSc, Bharathidasan University, 1992 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management Walden University May 2019 Abstract Despite the increased cost of data breaches due to advanced, persistent threats from malicious sources, the adoption of big data security analytics among U.S. small businesses has been slow.
Abstract: Analyzing Small Businesses’ Adoption of Big Data Security Analytics by Henry Mathias MCA, Anna University, 1995 BSc, Bharathidasan University, 1992 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management Walden University May 2019 Abstract Despite the increased cost of data breaches due to advanced, persistent threats from malicious sources, the adoption of big data security analytics among U.S. small businesses has been slow. Anchored in a diffusion of innovation theory, the purpose of this correlational study was to examine ways to increase the adoption of big data security analytics among small businesses in the United States by examining the relationship between small business leaders’ perceptions of big data security analytics and their adoption. The research questions were developed to determine how to increase the adoption of big data security analytics, which can be measured as a function of the user’s perceived attributes of innovation represented by the independent variables: relative advantage, compatibility, complexity, observability, and trialability. The study included a cross-sectional survey distributed online to a convenience sample of 165 small businesses. Pearson correlations and multiple linear regression were used to statistically understand relationships between variables. There were no significant positive correlations between relative advantage, compatibility, and the dependent variable adoption; however, there were significant negative correlations between complexity, trialability, and the adoption. There was also a significant positive correlation between observability and the adoption. The implications for positive social change include an increase in knowledge, skill sets, and jobs for employees and increased confidentiality, integrity, and availability of systems and data for small businesses. Social benefits include improved decision making for small businesses and increased secure transactions between systems by detecting and eliminating advanced, persistent threats.Despite the increased cost of data breaches due to advanced, persistent threats from malicious sources, the adoption of big data security analytics among U.S. small businesses has been slow. Anchored in a diffusion of innovation theory, the purpose of this correlational study was to examine ways to increase the adoption of big data security analytics among small businesses in the United States by examining the relationship between small business leaders’ perceptions of big data security analytics and their adoption. The research questions were developed to determine how to increase the adoption of big data security analytics, which can be measured as a function of the user’s perceived attributes of innovation represented by the independent variables: relative advantage, compatibility, complexity, observability, and trialability. The study included a cross-sectional survey distributed online to a convenience sample of 165 small businesses. Pearson correlations and multiple linear regression were used to statistically understand relationships between variables. There were no significant positive correlations between relative advantage, compatibility, and the dependent variable adoption; however, there were significant negative correlations between complexity, trialability, and the adoption. There was also a significant positive correlation between observability and the adoption. The implications for positive social change include an increase in knowledge, skill sets, and jobs for employees and increased confidentiality, integrity, and availability of systems and data for small businesses. Social benefits include improved decision making for small businesses and increased secure transactions between systems by detecting and eliminating advanced, persistent threats. Analyzing Small Businesses’ Adoption of Big Data Security Analytics by Henry Mathias MCA, Anna University, 1995 BSc, Bharathidasan University, 1992 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management Walden University May 2019 Dedication I thank the Lord Jesus Christ for the great privilege to begin and complete my doctoral dissertation. I dedicate this work first to the only immortal and invisible Christ who enabled me to pursue and achieve my academic dreams. I would also dedicate this work to my father, Mathias Moves, who was my constant inspiration and motivation in all my academic dreams and successes. Finally, I dedicate this dissertation to my wife, Christina Henry, who supported me all through these tough years of this doctoral journey. I would not have accomplished all these milestones without the constant support and encouragement from my family. Acknowledgments I would like to thank my Dissertation Chair, Dr. David Gould, for his outstanding guidance, continuous availability, and his constant motivation for me to succeed in this doctoral journey. His enthusiasm and genuine interest in making me successful, kept me energized and focused throughout my dissertation phase. I also would like to thank my Committee Member, Dr. Anthony Lolas, for his excellent input and guidance in the field of big data and quantitative methodologies. My special thanks to Dr. Thomas Butkiewicz, as my University Research Reviewer and for all his valuable contribution to my doctoral success. Above all, I thank my God and the Lord Jesus Christ for helping me to complete this lifelong lofty dream. I am exceedingly grateful to everyone who has helped me on this doctoral journey without whose assistance, I would not have completed my doctoral program. Every small help and guidance has enabled me to complete this monumental and substantial task of completing the dissertation. I thank God for this wonderful journey and for angels who helped me to achieve this great milestone in life.
4 citations
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TL;DR: In this paper, a decision support system integrating M&A activity information for strategic investment planning and identifying promising technologies in the Healthcare sector to manage the irregularities of market trend and investment outcome is presented.
Abstract: The existing approaches to identification of emerging technologies create a prominent opportunity for technology convergence and market growth potential However, existing approaches either suffer from the time lag issue or have yet to explorethe assessment’s uncertainty and ambiguity Based on a total of 14 years of mergers and acquisitions (M&A) activity data in the Health Care sector, the complex patterns between growth velocity and accelerating of M&A activities are analyzed with two quantitative indicators (Promising Index and Promising Index Sharpe Ratio) to identify emerging technological opportunities The proposed integrative approach offers a mean to resolve the time lag issue, deal with market trend irregularity, and manage expectations of investors for emerging technology and industry Specifically, this study aims to (i) provide a decision support system integrating M&A activity information for strategic investment planning and (ii) identify promising technologies in the Healthcare sector to manage the irregularities of market trend and investment outcome This study is one of the first research that employs a prior data-based approach to delineate emerging technologies by analyzing the growth momentum properties of specific industry areas based on the M&A activity data
1 citations
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TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
Abstract: The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.
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TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
Abstract: Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
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TL;DR: Big data, the authors write, is far more powerful than the analytics of the past, and executives can measure and therefore manage more precisely than ever before, and make better predictions and smarter decisions.
Abstract: Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster.
3,327 citations
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TL;DR: A few tools for manipulating and analyzing big data such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships.
Abstract: Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by econom...
912 citations
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TL;DR: A first step toward an inclusive big data research agenda for IS is offered by focusing on the interplay between big data’s characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS).
Abstract: Big data has received considerable attention from the information systems (IS) discipline over the past few years, with several recent commentaries, editorials, and special issue introductions on the topic appearing in leading IS outlets. These papers present varying perspectives on promising big data research topics and highlight some of the challenges that big data poses. In this editorial, we synthesize and contribute further to this discourse. We offer a first step toward an inclusive big data research agenda for IS by focusing on the interplay between big data’s characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS). We view big data as a disruption to the value chain that has widespread impacts, which include but are not limited to changing the way academics conduct scholarly work. Importantly, we critically discuss the opportunities and challenges for behavioral, design science, and economics of IS research and the emerging implications for theory and methodology arising due to big data’s disruptive effects.
434 citations
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