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

Author

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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)

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.

Suggested Citation

  • Andrea De Mauro & Marco Greco & Michele Grimaldi, 2019. "Understanding Big Data Through a Systematic Literature Review: The ITMI Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1433-1461, July.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:04:n:s0219622019300040
    DOI: 10.1142/S0219622019300040
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    3. Meir Russ, 2021. "Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models," Sustainability, MDPI, vol. 13(6), pages 1-32, March.
    4. Sestino, Andrea & Prete, Maria Irene & Piper, Luigi & Guido, Gianluigi, 2020. "Internet of Things and Big Data as enablers for business digitalization strategies," Technovation, Elsevier, vol. 98(C).

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