Taking a needed break to focus on getting healthy. Be back in August!

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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.

Mirela's LinkedIn: https://www.linkedin.com/in/mirelanavodaru/

In this episode, Scott interviewed Mirela Navodaru, Enterprise and Solution Architect for Data, Analytics, and AI at Swisscom.

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

1. Specifically at Swisscom, it's not about doing data mesh. They want to make data a key part of all their major decisions - operational and strategic - and data mesh means they can put the data production and consumption in far more people's hands. Data mesh is a way to achieve their data goals, not the goal.
2. When you are trying to get people bought in to something like data mesh, you always have to consider what is in it for them. Yes, the overall organization benefiting is great but it’s not the best selling point 😅 try to develop your approach to truly benefit everyone.
3. Data literacy is crucial to getting the most value from data mesh. Data mesh is not about throwing away the important knowledge your data people have but it's about unlocking the value of the knowledge your business people have to be shared with the rest of the organization effectively, reliably, and scalably.
4. ?Controversial? You really have to talk to a lot of people early in your data mesh journey to discover the broader benefits to the organization. That way you can talk to people's specific challenges to get them bought in. When designing your journey, it is important to get input from a large number of people.
5. When talking data as a product versus data products, the first is the core concept and the second is the deliverables. Scott note: this is a really simple but powerful delineation
6. "No value, no party." If there isn't a value proposition, there shouldn't be any action. You need to stay focused on value because there are so many potential places to focus in a data mesh implementation.
7. You have to balance value at the use case level to the domain versus more global value to the organization. At the end of the day, everything you do should add value to the organization but sometimes use cases are...

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:

1. ?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.
2. !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.
3. 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.
4. 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.
5. 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.
6. 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.
7. ?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...