When design meets data: Using Data Mesh for a consumer and business-driven data strategy

on YouTube ↗43m

" summary

Argues that flipping from a data-producer pipeline to a consumer-first Data Mesh cuts lead time and makes ROI measurable by building bounded data product units with owners, SLAs and output ports (dashboard, S3/AWS buckets, API). Shows a concrete case: a mobility goal to raise infrastructure capacity 40%, creates a TRACK data product that forecasts maintenance, specifies refresh cadence (daily) and cleanliness (example 50%), validates hypotheses with MVPs and two-week sprints, and uses OKRs and cost–benefit to decide what to build.

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" chapters (39)

1. 0:00Intro
2. 0:52Agenda
3. 1:35What is a data project
4. 3:20Data strategy approaches
5. 7:55Challenges
6. 14:05Product Thinking recap
7. 15:03Building a roadmap
8. 15:44A data product
9. 16:30Step 1 Align
10. 17:28Step 2 Define a hypothesis
11. 18:33Step 3 Define data products
12. 19:08What is Track
13. 20:38Evolving data products
14. 21:03Identifying new data products
15. 21:41grouping data products
16. 22:28identifying data products
17. 23:29product prioritization
18. 24:36data producer approach
19. 25:11data consumer approach
20. 25:56data mesh
21. 27:01how does this approach help
22. 27:38data engineering teams
23. 29:07definition of success
24. 30:10where is your focus
25. 30:38product checklist
26. 31:04complete the cycle
27. 31:38Questions
28. 31:50Technical issues
29. 32:46Crossfunctional teams
30. 33:28Selfserve data platform
31. 34:21Data owning team
32. 34:47Two layers of data
33. 35:54What is a data product
34. 36:11What sets the data product apart
35. 38:22Should I take it
36. 40:17Visibility
37. 40:43Agile vs Data Mesh
38. 41:28What kind of projects can use Data Mesh
39. 42:40Speed to market

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