Beyond the hype
TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.
About: This article is published in International Journal of Information Management.The article was published on 2015-04-01 and is currently open access. It has received 2962 citations till now. The article focuses on the topics: Analytics & Big data.
Citations
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Swansea University1, University of Bradford2, University of Newcastle3, Ryerson University4, University of Jyväskylä5, Paris School of Business6, University of Nevada, Las Vegas7, Symbiosis International University8, Shippensburg University of Pennsylvania9, Bundeswehr University Munich10, Manchester Metropolitan University11, University of Helsinki12, Florida Gulf Coast University13, University of Sheffield14
TL;DR: This research offers a significant and timely contribution to both researchers and practitioners in the form of challenges and opportunities where it highlights the limitations within the current research, outline the research gaps and develop the questions and propositions that can help advance knowledge within the domain of digital and social marketing.
588 citations
TL;DR: The Industry 4.0 environment is scanned on this paper, describing the so-called enabling technologies and systems over the manufacturing environment.
586 citations
TL;DR: This paper might be the first attempt to present a comprehensive literature review on different types of big data in tourism research, and facilitates a thorough understanding of this sunrise research and offers valuable insights into its future prospects.
585 citations
TL;DR: In this article, the authors identify factors influencing decision-making based on big data using a case study and show that taking advantage of big data is an evolutionary process in which the gradually understanding of the potential of Big Data and the routinization of processes plays a crucial role.
461 citations
Cites background from "Beyond the hype"
...Gandomi and Haider (2015) add three other Vs to this list; Value, Variability and Veracity....
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...BD is often related to predictive analytics which comprises a variety of techniques that predict future outcomes to uncover patterns and find relationships in data (Gandomi & Haider, 2015)....
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...Veracity refers to that data may be incomplete, out-ofdate and contains noise (Gandomi & Haider, 2015)....
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TL;DR: The present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains and identifies gaps in the extant literature and proposes six future research themes.
Abstract: With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.
431 citations
Cites background or methods from "Beyond the hype"
...Hence, big data used for business decisions should be authenticated and have passed through strict quality-compliance procedures before being analyzed (Dong & Strivastana, 2013; Gandomi & Haider, 2015)....
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...This vast amount of data is argued to be an important enabler of creating value for organizations (Gandomi & Haider, 2015)....
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...While there are no universal benchmarks for defining the volume, velocity, and variety of big data, the defining limits are contingent upon size, sector, and location of the firm, and are subject to changing limits over time (Gandomi & Haider, 2015)....
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References
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Book•
08 Jul 2008TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
7,452 citations
"Beyond the hype" refers background in this paper
...For example, sentiment analysis (opinion mining) have been known 1 l of In s n u T s t b h n i g d t m i o A s R A B B C C C D F F F G G H H H H H 44 A. Gandomi, M. Haider / International Journa ince the early 2000s (Pang & Lee, 2008)....
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Book•
13 May 2011
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.
4,700 citations
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.
4,610 citations
Book•
01 May 2012TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Abstract: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.
4,515 citations
TL;DR: In this article, the authors introduce the concept of sure screening and propose a sure screening method that is based on correlation learning, called sure independence screening, to reduce dimensionality from high to a moderate scale that is below the sample size.
Abstract: Summary. Variable selection plays an important role in high dimensional statistical modelling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or dimensionality p, accuracy of estimation and computational cost are two top concerns. Recently, Candes and Tao have proposed the Dantzig selector using L1-regularization and showed that it achieves the ideal risk up to a logarithmic factor log (p). Their innovative procedure and remarkable result are challenged when the dimensionality is ultrahigh as the factor log (p) can be large and their uniform uncertainty principle can fail. Motivated by these concerns, we introduce the concept of sure screening and propose a sure screening method that is based on correlation learning, called sure independence screening, to reduce dimensionality from high to a moderate scale that is below the sample size. In a fairly general asymptotic framework, correlation learning is shown to have the sure screening property for even exponentially growing dimensionality. As a methodological extension, iterative sure independence screening is also proposed to enhance its finite sample performance. With dimension reduced accurately from high to below sample size, variable selection can be improved on both speed and accuracy, and can then be accomplished by a well-developed method such as smoothly clipped absolute deviation, the Dantzig selector, lasso or adaptive lasso. The connections between these penalized least squares methods are also elucidated.
2,204 citations