Big data team process methodologies: A literature review and the identification of key factors for a project's success

Jeffrey Saltz, Ivan Shamshurin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations

Abstract

This paper reports on our review of published research relating to how teams work together to execute Big Data projects. Our findings suggest that there is no agreed upon standard for executing these projects but that there is a growing research focus in this area and that an improved process methodology would be useful. In addition, our synthesis also provides useful suggestions to help practitioners execute their projects, specifically our identified list of 33 important success factors for executing Big Data efforts, which are grouped by our six identified characteristics of a mature Big Data organization.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2872-2879
Number of pages8
ISBN (Electronic)9781467390040
DOIs
StatePublished - Feb 2 2017
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period12/5/1612/8/16

Keywords

  • Analytics Process
  • Big Data
  • Data Science
  • Process Methodology
  • Project Management

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Big data team process methodologies: A literature review and the identification of key factors for a project's success'. Together they form a unique fingerprint.

  • Cite this

    Saltz, J., & Shamshurin, I. (2017). Big data team process methodologies: A literature review and the identification of key factors for a project's success. In Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 2872-2879). [7840936] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2016.7840936