Understanding Big Data Through a Systematic Literature Review: The ITMI Model

Andrea De Mauro (), Marco Greco and Michele Grimaldi ()
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Andrea De Mauro: Department of Enterprise Engineering, University of Rome Tor Vergata, Rome, Italy
Marco Greco: #x2020;Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino (FR), Italy
Michele Grimaldi: #x2020;Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino (FR), Italy

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 04, 1433-1461

Abstract: The concept of Big Data in academic and professional literature has developed in a euphoric, chaotic, and unstructured manner. Decision-making is increasingly relying on Big Data, resorting to novel analytic methodologies that are applied in many different industries. This study aims to provide clarity over the Big Data phenomenon by means of a comprehensive and systematic literature review, able to produce a clear description of what Big Data is today, a structured classification of the various streams of current research, and a list of promising emerging trends. This study analyses a corpus of 4,327 articles through a novel combination of unsupervised algorithms that produces a hierarchical topic structure which empirically validates and enhances the “Information,” “Technology,” “Methods,” and “Impact” conceptual model of Big Data, identifying 17 fundamental topics and providing researchers and practitioners with a meaningful overview of the body of knowledge and a proposed research agenda.

Keywords: Big Data; business intelligence; analytics; information systems; systematic literature review; text mining (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S0219622019300040

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