#302 Finding and Delivering on a Good Initial Data Mesh Use Case - Interview w/ Basten Carmio

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In this episode, Scott interviewed Basten Carmio, Customer Delivery Architect of Data and Analytics at AWS Professional Services. To be clear, he was only representing his own views on the episode.

Some key takeaways/thoughts from Basten's point of view:

1. Your first use case - at the core - should A) deliver value in and of itself and B) improve your capabilities to deliver on incremental use cases. That's balancing value delivery, improving capabilities, and building momentum which are all key to a successful long-term mesh implementation.
2. When thinking about data mesh - or really any tech initiative - it's crucial to understand your starting state, not just your target end state. You need to adjust any approach to your realities and make incremental progress.
3. ?Controversial?: Relatedly, it's very important to define what success looks like. Doing data mesh cannot be the goal. You need to consider your maturity levels and where you want to focus and what will deliver value for your organization. That is different for each organization. Scott note: this shouldn't be controversial but many companies are not defining their mesh value bet…
4. Even aligning everyone on your organization's definition of mesh success will probably be hard. But it's important to do.
5. For a data mesh readiness assessment, consider where you can deliver incremental value and align it to your general business strategy. If you aren't ready to build incrementally, you aren't going to do well with data mesh.
6. A common value theme for data mesh implementations is easier collaboration across the organization through data; that leads to faster reactions to changes and opportunities in your markets. Mesh done well means it's far faster and easier for lines of business to collaborate with each other - especially in a reliable and scalable way - and there are far better standard rules/policies/ways of working around that collaboration. But organizations have to see value in that or there may be mesh resistance.
7. As many have said, you must approach data mesh in a thin slice. Trying to focus too much on any pillar at the expense of the others leads to challenges. Scott note: Zhamak literally has a figure on this she shares often. It's easy to get unbalanced if you ignore a principle and fixing that takes more effort than thin slicing.
8. ?Controversial?: As you build out your mesh capabilities, especially your platform, think about what you need to actually deliver on the use case(s) at hand and deliver only that. Don't get ahead of yourself. It's fun to build capabilities but it's easy to build a monstrosity of a platform instead of proper abstractions to make building and managing data products simple.
9. Doing data mesh well is all about managing trade-offs. There are trade-offs at the use case and implementation level. And it's okay to get those wrong, just look to fail fast rather than hold on to bad decisions. Don't be precious with your decisions and build in ways to make evolving and improving easier.
10. MVP can be minimum viable product or minimum valuable product. You need to define what value actually means for each use case. It's easy to point to pain and start solutioning but not end up addressing the pain or creating value. This will help you prioritize and deliver the most value compared to effort early.
11. Relatedly, having a clear perspective on value in your MVP means you are more easily able to change and adapt as you learn and deliver. If you thought value was going to come from X but early indications are Y is way more important or is much different than expectations, you can more easily pivot towards Y.
12. Data products don't magically create value. They should be bets on value delivery. So you need to have ways to gracefully retire data products when the cost exceeds the incremental value. Often times a bet was a good bet even when it didn't pay off or it is no longer paying off.
13. Continuous communication and driving buy-in, especially with C-level execs, is unfortunately crucial to your data mesh implementation success. If they don't value better data capabilities, few people will invest their time and effort to really reinvent the way your organization does data.
14. Relatedly, you need to tie your data mesh implementation to the overall business strategy. Execs need to know why you are doing something like data mesh and how it will deliver value. It's not an overnight success but it also can't be a 3 year project before value. Speaking to that gets more people to lean in so you can build momentum.
15. Finding and leveraging your data success champions is always hard but it's necessary to build that momentum. Think about building champions quite early.
16. When you're trying to get an exec to buy in to something like data mesh, always put it in terms of what's in it for them. Don't try to get them bought in to some grand vision first, why would they invest their valuable time and resources here? Find their pain points and what something like data mesh will do to address those specifically.
17. Relatedly, trying to get someone to just take on data ownership without the understanding of what the rest of the organization is going to do to make ownership much easier - mainly through the platform and federated governance - is probably going to be a … not fun conversation.
18. When thinking about data mesh, it should all come back to value. If you can't specifically point to value that will exceed your effort for doing something like data mesh, it's probably not worth it for your organization. What is valuable to your organization and how will data mesh help you capture that value?
19. Relatedly, as you are on your data mesh journey, you have to be honest in assessing if data mesh is really working for your organization. It's okay to stop if it's not.
20. The data mesh principles sound really great and helpful in the abstract. But they just might not be aligned to value with the way your company does business.
21. ?Controversial?: It's perfectly acceptable to have hybrid approaches in a data mesh journey. There is a perception that everything should be decentralized and that just isn't always the best approach, don't get caught up in dogma. Scott note: the "decentralize everything" is a misunderstanding of Zhamak. Also, as you are on a journey, your current state won't match an ideal end state. Focus on evolving while delivering value, not trying to be perfect today instead of _getting_ to good.

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