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About: Analytics is a(n) research topic. Over the lifetime, 26208 publication(s) have been published within this topic receiving 391574 citation(s). The topic is also known as: data analytics & business intelligence. more


Journal ArticleDOI: 10.2307/41703503
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. more

Topics: Business intelligence (64%), Business analytics (59%), Analytics (55%) more

4,008 Citations

Open accessJournal ArticleDOI: 10.1080/1369118X.2012.678878
danah boyd1, Kate Crawford1Institutions (1)
Abstract: The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-tech... more

Topics: Big data (55%), Analytics (54%), Digital sociology (53%) more

3,432 Citations

Open accessJournal Article
Andrew McAfee1, Erik BrynjolfssonInstitutions (1)
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. more

Topics: Big data (61%), Analytics (60%), Information management (53%) more

3,327 Citations

Journal ArticleDOI: 10.1016/J.MFGLET.2014.12.001
Jay Lee1, Behrad Bagheri1, Hung-An Kao1Institutions (1)
Abstract: Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS. more

Topics: Cyber-physical system (60%), Industry 4.0 (55%), Analytics (52%)

2,716 Citations

Open accessJournal ArticleDOI: 10.1016/J.IJINFOMGT.2014.10.007
Amir H. Gandomi1, Murtaza Haider1Institutions (1)
Abstract: We define what is meant by big data.We review analytics techniques for text, audio, video, and social media data.We make the case for new statistical techniques for big data.We highlight the expected future developments in big data analytics. Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation. more

Topics: Analytics (65%), Big data (64%), Unstructured data (61%) more

2,385 Citations

No. of papers in the topic in previous years

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Topic's top 5 most impactful authors

Dragan Gašević

32 papers, 1.8K citations

Daniel A. Keim

30 papers, 2.6K citations

Simon Buckingham Shum

28 papers, 859 citations

Alfredo Cuzzocrea

26 papers, 571 citations

Roberto Martinez-Maldonado

15 papers, 148 citations

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