\
 

Start Date

16-8-2018 12:00 AM

Description

There are many reasons data science teams should use a well-defined process to manage and coordinate their efforts, such as improved collaboration, efficiency and stakeholder communication. This paper explores the current methodology data science teams use to manage and coordinate their efforts. Unfortunately, based on our survey results, most data science teams currently use an ad hoc project management approach. In fact, 82% of the data scientists surveyed did not follow an explicit process. However, it is encouraging to note that 85% of the respondents thought that adopting an improved process methodology would improve the teams’ results. Based on these results, we described six possible process methodologies teams could use. To conclude, we outlined plans to describe best practices for data science team processes and to develop a process evaluation framework.

Share

COinS
 
Aug 16th, 12:00 AM

Exploring Project Management Methodologies Used Within Data Science Teams

There are many reasons data science teams should use a well-defined process to manage and coordinate their efforts, such as improved collaboration, efficiency and stakeholder communication. This paper explores the current methodology data science teams use to manage and coordinate their efforts. Unfortunately, based on our survey results, most data science teams currently use an ad hoc project management approach. In fact, 82% of the data scientists surveyed did not follow an explicit process. However, it is encouraging to note that 85% of the respondents thought that adopting an improved process methodology would improve the teams’ results. Based on these results, we described six possible process methodologies teams could use. To conclude, we outlined plans to describe best practices for data science team processes and to develop a process evaluation framework.