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Prescriptive Analytics: A Survey of Approaches and Methods

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 339))

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Abstract

Data analytics has gathered a lot of attention during the last years. Although descriptive and predictive analytics have become well-established areas, prescriptive analytics has just started to emerge in an increasing rate. In this paper, we present a literature review on prescriptive analytics, we frame the prescriptive analytics lifecycle and we identify the existing research challenges on this topic. To the best of our knowledge, this is the first literature review on prescriptive analytics. Until now, prescriptive analytics applications are usually developed in an ad-hoc way with limited capabilities of adaptation to the dynamic and complex nature of today’s enterprises. Moreover, there is a loose integration with predictive analytics, something which does not enable the exploitation of the full potential of big data.

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Acknowledgements

This work is partly funded by the European Commission project H2020 UPTIME “Unified Predictive Maintenance System” (768634).

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Authors and Affiliations

  1. Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), 9 Iroon Polytechniou Street, 157 80, Zografou, Athens, Greece

    Katerina Lepenioti, Alexandros Bousdekis, Dimitris Apostolou & Gregoris Mentzas

  2. Department of Informatics, University of Piraeus, 80 Karaoli & Dimitriou Street, 185 34, Piraeus, Greece

    Dimitris Apostolou

Authors
  1. Katerina Lepenioti
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  2. Alexandros Bousdekis
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  3. Dimitris Apostolou
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  4. Gregoris Mentzas
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Correspondence to Katerina Lepenioti .

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Editors and Affiliations

  1. Poznań University of Economics and Business, Poznań, Poland

    Witold Abramowicz

  2. Fraunhofer FOKUS, Berlin, Berlin, Germany

    Adrian Paschke

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Lepenioti, K., Bousdekis, A., Apostolou, D., Mentzas, G. (2019). Prescriptive Analytics: A Survey of Approaches and Methods. In: Abramowicz, W., Paschke, A. (eds) Business Information Systems Workshops. BIS 2018. Lecture Notes in Business Information Processing, vol 339. Springer, Cham. https://doi.org/10.1007/978-3-030-04849-5_39

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  • DOI: https://doi.org/10.1007/978-3-030-04849-5_39

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