Beyond the hype: Big data concepts, methods, and analytics

•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 t...

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Bibliographic Details
Published in:International journal of information management Vol. 35; no. 2; pp. 137 - 144
Main Authors: Gandomi, Amir, Haider, Murtaza
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.04.2015
Elsevier Science Ltd
Subjects:
Academic disciplines
Academic staff
Algorithms
Big Data
Big data analytics
Big data definition
Computational efficiency
Correlation
Correlation analysis
Data analysis
Data calls
Data collection
Datasets
Definitions
Format
Heterogeneity
Information science
Methods
Predictive analytics
Samples
Statistical methods
Studies
Texts
Unstructured data analytics
Big data analytics
Big data definition
Predictive analytics
Unstructured data analytics
ISSN:0268-4012, 1873-4707
Online Access:Get full text
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Summary:•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.
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ISSN:0268-4012
1873-4707
DOI:10.1016/j.ijinfomgt.2014.10.007