@inproceedings{406da663b3b24614bb2392ff7c2c4090,
title = "SKI: An Agile Framework for Data Science",
abstract = "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.",
keywords = "Agile, Big Data, Data Science, Process Methodology",
author = "Jeffrey Saltz and Alex Suthrland",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9005591",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3468--3476",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
}