SKI: An Agile Framework for Data Science

Jeffrey Saltz, Alex Suthrland

Research output: Chapter in Book/Entry/PoemConference contribution

10 Scopus citations


This paper explores data science project management by first noting the need for a new process management framework and then defines a process framework that effectively supports the needs of a data science team. The paper also reports on a pilot study of teams using the framework. The framework adheres to the lean Kanban philosophy but augments Kanban by providing a structured iteration process for teams to incrementally explore and learn via lean hypothesis testing. Specifically, the Structured Kanban Iteration (SKI) framework focuses on having teams define capability-based iterations (as opposed to Kanban-like no iterations or Scrumlike time-based sprints). Furthermore, unlike Kanban, the framework leverages Scrum best practices to define roles, meetings and artifacts. Thus, SKI implements the Kanban process, but with a more repeatable and structured approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Electronic)9781728108582
StatePublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019


Conference2019 IEEE International Conference on Big Data, Big Data 2019
Country/TerritoryUnited States
CityLos Angeles


  • Agile
  • Big Data
  • Data Science
  • Process Methodology

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management


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