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UX, DX, DSX: Developers and Data Scientists as Users


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More and more companies nowadays are investing heavily in building infrastructure for developers and data scientists. But often, building infrastructure products are treated as pure engineering practices and differentiated from feature products.

I would like to share my experience leading a team at BuzzFeed in building user-centric infrastructure products for our developers and data scientists, and how I integrate and adapt traditional PM techniques for technical products.

Building software for our peers is a double-edged sword. On one hand, our users are technologists themselves and have immense appreciation for well-designed infrastructure and tools. On the other hand, it is very tempting for us as developers to make assumptions about those folks with whom we work closely. When building tools for data scientists, it is especially crucial to keep in mind that they have their own distinct workflows and needs.

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UX, DX, DSX: Developers and Data Scientists as Users

  1. 1. UX, DX, DSX Building tools and infrastructure for developers and data scientists Dot Gong
  2. 2. Data Gives Us Superpower
  4. 4. Building for the Company that Runs on Data
  5. 5. ๏ฟฝ๏ฟฝ An Engineer / A User
  6. 6. ๐Ÿฅ•๐Ÿฅ•๐Ÿฅ•๐Ÿฅ•๐Ÿฅ•๐Ÿฅ•๐Ÿฅ•๐Ÿฅ•๐Ÿฅ•๐Ÿฅ• ๐Ÿฅ• ๐Ÿฅ•๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ ๐Ÿฅ• ๐Ÿฅ•๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ๐Ÿฐ ๐Ÿฅ•
  7. 7. ๐Ÿผ๐Ÿฐ๐Ÿฆ๐Ÿป๐Ÿฑ๐Ÿฐ๐Ÿผ๐Ÿฐ ๐Ÿฐ๐ŸฆŠ๐Ÿฐ๐Ÿฐ๐Ÿท๐Ÿฆ๐Ÿฎ๐Ÿฆ ๐Ÿฐ๐Ÿป๐Ÿฐ๐Ÿต ๐ŸฆŠ๐Ÿฐ๐Ÿผ๐Ÿต๐Ÿฑ๐Ÿป๐Ÿฐ๐Ÿต Full Spectrum of SWE
  9. 9. Every single company I've worked at and talked to has the same problem without a single exception so far โ€” poor data quality, especially tracking data. Ruslan Belkin VP of Engineering, โ€œ โ€œ @peterskomoroch โ€œProduct Management for AIโ€
  10. 10. Improve DX to Solve Data Problems โ— Improve Front-end tools to make data collection intuitive and error-prone. โ— Provide faster feedback to make data validation seamless in dev process. โ— Show them the context and the resulting analysis / product of data collection. โ— Start the conversation between data producers and consumers.
  11. 11. Align Producer and Consumer Incentives
  12. 12. Engineers Data Scientists
  13. 13. UX Research for Technical Users
  14. 14. Traditional UX Research Techniques
  15. 15. Engineering Speci๏ฌc Techniques Github issues, branches and PRs provide a great log of developer actions.
  16. 16. Engineering Speci๏ฌc Techniques Search in Slack or other messaging tools for developer blockers.
  17. 17. Visualize Your Findings โ— Visually represent pain points โ—‹ Executive buy-in โ—‹ Users feel heard โ— Establish common language
  18. 18. Adapt Your Organization
  19. 19. E๏ฌ€ective Way to Structure Your Team โ— Organization is part of developer experience and data science experience. โ— Sometimes the solution to solving developer/data scientists speed bump is organizational. โ— There is a lot of discussion on this topic but no one-size-๏ฌts-all solution.
  20. 20. Hybrid Model: Adjust based on Organization Maturity EMBEDDEDCENTRALIZED
  21. 21. Two New Data Roles โ— Strategic Data Partner โ—‹ Each data scientist is assigned one product area โ—‹ Becomes domain expert โ—‹ Conducts quarterly data review โ— Data Steward โ—‹ Establish cross-org data standards (governance, instrumentation, namespace) โ—‹ Guide data responsibility for their respective products โ—‹ Usually an Engineer or a PM
  22. 22. The Data Science Hierarchy of Needs Monica Rogati โ€œThe AI Hierarchy of Needsโ€ LEARN/OPTIMIZE AGGREGATE/LABEL EXPLORE/TRANSFORM MOVE/STORE COLLECT PROJECT 1 PROJECT 2
  23. 23. Recap
  24. 24. Recap โ— Move your subjectivity. โ— Empathize and listen to your users. โ— Find new methods to learn more about them. โ— Structure your organization best to ๏ฌt your teams needs. P.S. We are hiring in NYC, LA, and London.
  25. 25. Thank You!