Please Rate and Review us on your podcast app of choice!

Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.

Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.

Alyona's LinkedIn: https://www.linkedin.com/in/alyonagalyeva/

In this episode, Scott interviewed Alyona Galyeva, Principal Data Engineer at Thoughtworks. To be clear, she was only representing her own views on the episode.

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

?Controversial? People keep coming up with simple phrasing and a few sentences about where to focus in data mesh. But if you're headed in the right direction, data mesh will be hard, it's a big change. You might want things to be simple but simplistic answers aren't really going to lead to lasting, high-value change to the way your org does data. Be prepared to put in the effort to make mesh a success at your organization, not a few magic answers.
!Controversial! Stop focusing so much on the data work as the point. It's a way to derive and deliver value but the data work isn't the value itself.
Relatedly, ask what are the key decisions people need to make and what is currently preventing them from making those decisions. Those are likely to be your best use cases.
When it comes to Zhamak's data mesh book, it needs to be used as a source of inspiration instead of trying to use it as a manual. Large concepts like data mesh cannot be copy/paste, they must be adapted to your organization.
It's really important to understand your internal data flows. Many people inside organizations - especially the data people - think they know the way data flows across the organization, especially for key use cases. But when you dig in, they don't. Those are some key places to deeply investigate first to add value.
On centralization versus decentralization, it's better to think of each decision as a slider rather than one or the other. You need to find your balances and also it's okay to take your time as you shift more towards decentralization for many aspects. Change management is best done incrementally.
?Controversial? A major misunderstanding of data mesh that some long-time data people have is that it is just sticking a better self-serve consumption layer on top and we can continue to do monolithic data work under the hood. Be prepared for lots of friction in convincing some data architects that this isn't just a reskin or another layer on top of the enterprise warehouse or data lake.
For data mesh, it's crucial to understand necessary changes at the technical and the business level. You can't only work on one but you also don't have to 'solve' them at the start, make progress. It's like with the four principles, you need to thin slice change across the technical and business aspects rather than only focusing on one.
You can sell data engineers on data mesh by making their work more meaningful and impactful. Instead of mostly firefighting - which is the case in many organizations - they can focus on shipping new features and adding incremental value.
With data mesh, you want people focusing on more than just making data valuable - what is valuable will change so how do you make your data products evolvable and maintainable?
You always want to be focused on addressing people's pain points in data mesh, driving towards value. That's how you can get data people bought in as well, not just business people.
!Controversial! Doing aggregated data products across domains is usually the data mesh inflection point - basically answering can data mesh work in your organization. If you can't get that cross domain collaboration going well, you should consider another model like hub and spoke.
Relatedly, aggregating data across multiple domains is where there is usually the most value for an organization. But it's very hard to find good champions there because you need more vision and more hard work to collaborate across domain boundaries. Identify the people with the vision early in your journey, even if it's often better to actually only start working with them once you have more momentum and data products.
Too often, there is a rush to build _something_ instead of the right thing. Don't get fooled by the idea that data work always creates value. Even if the client or business partner asks you to build something of value, always circle back to the use cases. As much as we'd like to build universal data products, they just don't exist.
Relatedly though, don’t get so focused only on trying to build the consumer-aligned data products for hyper-specific use cases that you miss the forest for the trees. Sometimes the use case is something like 'we need to understand what data we even have to be able to use it to address our current problems in XYZ business line.'
To kind of sum it up: stop focusing on what you can build first. Focus on what you should build and then look at the realities. What matters to the business and why? Then focus on what's possible and what will deliver sustainable and maintainable value through data work.

Learn more about Data Mesh Understanding: https://datameshunderstanding.com/about

Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him on LinkedIn: https://www.linkedin.com/in/scotthirleman/

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

The Smithsonian, Great Idea (1846)

This Day

Political Implications and Funding of the Smithsonian Institution

The AI-powered Podcast Player