Identifying the key drivers for teams to use a data science process methodology

Research output: Contribution to conferencePaper

Abstract

While data science teams do not yet typically use a standard team process methodology, researchers are starting to explore process methodologies that improve team performance. However, little has been done to understand what might be the key acceptance factors for teams to implement a data science process methodology. To address this gap, the Diffusion of Innovation Theory is used as a theoretical lens to identify factors that might drive an organization to adopt a data science process methodology. The results of this qualitative research effort found ten factors that can influence a team to use, or not use, a data science process methodology. In short, eight positive factors were found with respect to relative advantage and compatibility and two negative factors were identified with respect to complexity. While more work is required to validate and refine these factors, the derived acceptance model can help teams as they consider adopting an improved data science process methodology.

Original languageEnglish (US)
StatePublished - Jan 1 2018
Event26th European Conference on Information Systems, ECIS 2018 - Portsmouth, United Kingdom
Duration: Jun 23 2018Jun 28 2018

Conference

Conference26th European Conference on Information Systems, ECIS 2018
CountryUnited Kingdom
CityPortsmouth
Period6/23/186/28/18

    Fingerprint

Keywords

  • Big Data
  • Data Science
  • Project Management

ASJC Scopus subject areas

  • Information Systems

Cite this

Saltz, J. (2018). Identifying the key drivers for teams to use a data science process methodology. Paper presented at 26th European Conference on Information Systems, ECIS 2018, Portsmouth, United Kingdom.