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Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here.

Omar's LinkedIn: https://www.linkedin.com/in/kmaomar/

Omar's State of Data Mesh presentation: https://www.youtube.com/watch?v=S5ABE4bevn4

Adam Grant book Think Again: The Power of Knowing What You Don't Know: https://www.amazon.com/Think-Again-Power-Knowing-What/dp/B08HJQHNH9

Lean Value Tree definition: https://openpracticelibrary.com/practice/lean-value-tree/

In this episode, Scott interviewed Omar Khawaja, Head of Business Intelligence at Roche Diagnostics. To be clear, Omar was only representing his own viewpoints and learnings, not necessarily those of Roche.

Some interesting thoughts/takeaways from Omar's point of view and learnings:

1. If you are going to make progress in a data mesh journey, you must be okay with "good enough". Perfect is the enemy of good and done. Measure, learn, and adjust along the way but get moving and keep moving. It's okay to make mistakes - recognize and correct them.
2. Echoing a number of past guests, change management and organizational challenges will take a large portion of a data mesh implementation leader's time and effort - likely far more than most would expect. Focus on empowering people and showing them why this can work for them. And what it means for them.
3. Data mesh cannot be your entire data strategy. If you are implementing data mesh, it must only be part of your data strategy. Start from the why. Why undertake something as transformational and difficult as implementing data mesh? What business value will it deliver?
4. Data-as-a-product thinking is the true heart of a data mesh implementation. It's far more than just creating data products. Data product discovery is crucial, much like discovery in regular product management. Take considerable learnings from product management in other disciplines.
5. Focus on outcomes in day-to-day data work. What are you trying to deliver? What is the value in...