Exploring the process of doing data science via an ethnographic study of a media advertising company

Jeffrey Saltz, Ivan Shamshurin

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

14 Scopus citations

Abstract

This paper presents the results of an ethnographic study focused on how data science projects were conducted within a global media advertising company. Observations, via embedding a researcher within the team, as well as more structured interviews and surveys, are documented. Recommendations to improve the current data science methodology within the company are also discussed. Overall, there had been little focus on the team's process methodology and the suggested process improvements would result in the company's data science projects having less risk and shorter timelines. Other big data teams might also benefit from reviewing and refining their work processes, but more work needs to be done to validate this assumption.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2098-2105
Number of pages8
ISBN (Electronic)9781479999255
DOIs
StatePublished - Dec 22 2015
Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
Duration: Oct 29 2015Nov 1 2015

Other

Other3rd IEEE International Conference on Big Data, IEEE Big Data 2015
CountryUnited States
CitySanta Clara
Period10/29/1511/1/15

Keywords

  • Big Data
  • Data Science
  • Process Methodology

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

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    Saltz, J., & Shamshurin, I. (2015). Exploring the process of doing data science via an ethnographic study of a media advertising company. In Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 2098-2105). [7363992] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2015.7363992