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Conceptualization of Predictive Analytics by Literature Review

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Data-Centric Business and Applications

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 30))

Abstract

Predictive Analytics, together with Big Data Analytics, learning algorithms, and machine learning are the most advanced technical innovations of this time. The notion of predictive analytics was introduced in the 20th century and become more and more expanded and applied in many fields like healthcare, business, supply chain management, telecommunications, and many others. The aim of this paper is a detailed literature analysis on Predictive Analytics, mainly in articles published in relevant journals during the selected time period, from 2010 till now. Various databases were used in order to find the most relevant articles for this topic. Articles were systematically analyzed regarding the author (authors), year of publication, the area of research, output, and journal, where the article was published. The main contribution of this article is evidence of the most relevant articles related to Predictive analytics, which can be used for every reader and also an overview, where, or in which fields Predictive analytics is applied and how was used during last years in various researches.

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Author information

Authors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Katarína Močarníková & Michal Greguš

Authors
  1. Katarína Močarníková
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  2. Michal Greguš
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Correspondence to Katarína Močarníková .

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Editors and Affiliations

  1. Department of e-Business, Faculty of Business, Economics and Statistics, University of Vienna, Vienna, Wien, Austria

    Natalia Kryvinska

  2. Department of Information Systems, Faculty of Management, Comenius University, Bratislava, Slovakia

    Michal Greguš

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Močarníková, K., Greguš, M. (2020). Conceptualization of Predictive Analytics by Literature Review. In: Kryvinska, N., Greguš, M. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-19069-9_8

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