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

By Amir Gandomi, Murtaza Haider. International Journal of Information Management, 2015

3,918 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Information Systems and Management, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1bc34cb22131554ba18f6ba9e6ede5beb42939f1

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Citations in the corpus

Setting the future of digital and social media marketing research: Perspectives and research propositions (2020, background): “…has been researched in relation to electronic word-of-mouth (Sohn, 2009), identifying influencers (Zhang & Li, 2014) and investigating how individuals influence others, the relationship between influence and tie strength and the flow of information through social media (Gandomi & Haider, 2015).”
Big data techniques in auditing research and practice: Current trends and future opportunities (2018, neutral): “The rapid growth of big data across all fields means that academic publications have been leapfrogged by discourse in popular outlets Gandomi and Haider (2015).”
The impact of big data analytics on firms’ high value business performance (2016, background): “…the techniques, technologies, systems, practices, methodologies, and applications that analyze great variety of critical business data to help a firm better understand its business and market, and make timely and effective business decisions (Gandomi and Haider 2015; Mcafee and Brynjolfsson 2012).”
A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications (2019, background): “The DT exploits predictive analytics [105], [106] to predict future statuses and important changes (such as failures) in the product lifecycle.”
Digital Disruption beyond Uber and Airbnb—Tracking the long tail of the sharing economy (2020, methodology): “…approach that combines, extends, and adapts methods for analysis of social media data (Stieglitz et al., 2014; cf. Jung et al., 2017; Brem and Bilgram, 2015) that has been introduced as a methodological alternative relatively recently (cf. Chen et al., 2012; Gandomi and Haider, 2015).”
The value of online surveys: a look back and a look ahead (2018, background): “Important research in this area relates to: big data analytics challenges (Lee, 2017; Pouyanfar et al., 2018), big data reduction methods (Rehman et al. (2016), customer experiences on social media (Ting et al., 2017) and debunking myths (Gandomi and Haider, 2015; Kimble and Milolidakis, 2015).”
Machine Learning With Big Data: Challenges and Approaches (2017, background): “This statistical challenge is also often described as spurious correlation [19].”
Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, background): “Furthermore, customer buying predictions and social media trends are analyzed through predictive analytics [61] (see Table 2).”
Uncertainty in big data analytics: survey, opportunities, and challenges (2019, background): “, NoSQL databases) contains tags to separate data elements [23, 26], but enforcing this structure is left to the database user.”
Critical analysis of Big Data challenges and analytical methods (2017, methodology): “Gandomi and Haider (2015) asserts the need to develop new solutions for predictive analytics for structured BD. Predictive analytics are principally based on statistical methods and seeks to uncover patterns and capture relationships in data. Gandomi and Haider (2015) categorised predictive analysis”