Managing Data Science as a Capability
By Domino2018-03-225 min read
Nick Elprin, CEO at Domino, presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and interactive breakouts. The learnings, anecdotes, and best practices shared in the workshop were based upon years of candid discussions with customers about managing and accelerating data science work. The workshop also featured reusable templates that included a pre-flight data science project checklist as well as a planning template for hiring and onboarding data scientists. We are sharing the breakout materials based on attendee feedback. If you missed Strata and are interested in joining similar discussions, then consider attending Rev.
Known Challenges with Managing Data Science
Challenges associated with hiring, building, managing, and leading data science teams have been discussed for years. Just one example includes the pain that early data science teams have experienced when switching from centralized to embedded team structures. Each company has unique needs and requirements. Companies with early data science teams tried different team structures in order to align data science work to their overall strategic vision or business value requirements. This known struggle is still present today. Many data science leaders and their organizations still discuss how to align data science work to key stakeholders’ needs and overall organizational business value. Yet, what if product management-inspired insights and templates were available to support people managing data science? Would that contribution be helpful? These are just a couple of the questions we asked ourselves at Domino.
Sharing Best Practices and Product Management-Inspired Templates
As data science matures as a discipline, we, as an overall industry, are able to learn from those who have gone before us. This enables us to share practical best practices for building, managing, and growing data science as a capability within an enterprise organization. At a recent industry conference, Domino’s CEO Nick Elprin presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and interactive breakouts. The learnings, anecdotes, and best practices shared in the workshop were based upon years of candid discussions with customers about managing and accelerating data science work. The workshop also featured reusable templates for the interactive breakouts that included a pre-flight data science project checklist as well as a planning template for hiring and onboarding data scientists.
A few insights shared at the in-depth workshop include
- Consider using a framework for managing data science as a capability that is anchored in people, process, technology, and x-factors.
- Based on discussions with our customers, we learned that applying product management principles to data science projects will save time in development as well as production.
- Using an artifact like a kickoff or pre-flight project checklist, will help maximize the probability of the data science work being aligned to business value and overall success. This checklist is inspired by a PRD (product requirements document) used within product management.
- Find, train, and retain people for your data science team. Multiple factors are needed to manage and grow a data science team. Consider utilizing a series of questions provided in the hiring and onboarding planning template to cultivate the right team aligned to your company’s strategic vision.
While the workshop training was based upon Domino’s "Practical Guide to Managing Data Science at Scale", the interactive workshop also provided reusable templates (pre-flight checklist as well as a hiring and onboarding planning template). Based on attendee requests for additional copies to share with their team members, we are providing the templates available for download. These templates are tools to help current and aspiring data science leaders grow data science as a capability within their orgs. Also, if you missed the workshop at Strata and would like to join the discussion at a similar depth and scope, you may be interested in attending the upcoming Rev.
Domino powers model-driven businesses with its leading Enterprise MLOps platform that accelerates the development and deployment of data science work while increasing collaboration and governance. More than 20 percent of the Fortune 100 count on Domino to help scale data science, turning it into a competitive advantage. Founded in 2013, Domino is backed by Sequoia Capital and other leading investors.
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