November 28, 2019

306 words 2 mins read

Building a data science idea factory: How to prioritize the portfolio of a large, diverse, and opinionated data science team

Building a data science idea factory: How to prioritize the portfolio of a large, diverse, and opinionated data science team

A huge challenge for data science managers is determining priorities for their teams, which often have more good ideas than they have time. Katie Malone and Skipper Seabold share a framework that their large and diverse data science team uses to identify, discuss, select, and manage data science projects for a fast-moving startup.
Talk Title Building a data science idea factory: How to prioritize the portfolio of a large, diverse, and opinionated data science team
Speakers Katie Malone (Civis Analytics), Skipper Seabold (Civis Analytics)
Conference Strata Data Conference
Conf Tag Big Data Expo
Location San Jose, California
Date March 6-8, 2018
URL Talk Page
Slides Talk Slides
Video

Data science is a field where the expectations are high but the guidance around how to deliver “data science impact” can be low. What are the most important projects for a data science team to work on? How can people with technical context and people with business context bring their priorities together in a common discussion? How does a data science team ensure that all team members get their best ideas heard by the organization? Civis Analytics’s data science research and development team consists of data scientists working on a wide variety of data science software and consulting tasks. One challenge the team confronted together (as it tripled in size) was how to prioritize projects in the company’s data science portfolio. Collectively, the team has better ideas and more experience than any single member alone, which suggests a bottom-up approach to sourcing project ideas. However, the team found that delivering a few high-quality, high-impact deliverables is better for the organization than lots of smaller, disorganized projects, which invites a more top-down approach. Katie Malone and Skipper Seabold share a framework and best practices for quickly and collaboratively proposing, discussing, selecting, and managing high-impact data science projects. Topics include:

framework css data science analytics collaborative
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