Data Mesh Radio

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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.

My overall point here is that why do so many folks in data hate Agile? Because in data it's so rarely done well basically. Things are done because 'that is the way they are supposed to be done' instead of 'because this will make our teams happier and more efficient'. And quite honestly, Agile isn't for every organization. The spirit of Agile probably should be for every organization so maybe go read the Agile manifesto but in data, the one size fits all approaches are obviously breaking more and more. So work with your teams and talk about what you want to achieve and collaborate with them to get there. Yes, easier said than done but I believe in you.

Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him at community at datameshlearning.com or 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/

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

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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

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 / 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. You can download their Data Mesh for Dummies e-book (info gated) here.

Tina's LinkedIn: https://www.linkedin.com/in/christina-albrecht-69a6833a/

In this episode, Scott interviewed Tina Albrecht, Lead Coach for Data-Driven Transformation at Exxeta.

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

1. Always start from your value chain - how do you actually generate value from data work? Any process or other tool you attempt to leverage that isn't focused on improving your data value chains will likely be ineffective in generating value. And why do data work if not to generate value?
2. Your two most likely reasons you are losing value in your value chain are lack of clear ownership/responsibility and bottlenecks. Look to regularly assess both.
3. When measuring if things are good enough, generally the DORA KPIs are good measures of data process maturity. But also look at two aspects: A) how happy are people (from customers, decision takers up to the team) with the current process. Satisfaction is a great measuring stick because it is highly correlated to effectiveness. And B) how much effectiveness is lost to bottlenecks and constraints.
4. The two ways most data mesh implementations seem to be going wrong are a misinterpretation of Team Topologies and lack of teams owning responsibilities. On the first, there are often breakdowns in how teams collaborate together and on the second, we need the platform team to own enabling domains but the domains keep trying to push work back to the central platform team.
5. It's important to regularly assess if aspects of your data transformation are good enough for now. But it's also very important - and easy to lose sight of - how are your teams feeling during the transformation. If you...

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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

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 / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

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

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

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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

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 / 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. You can download their Data Mesh for Dummies e-book (info gated) here.

Paolo's LinkedIn: https://www.linkedin.com/in/paoloplatter/

Paolo's Medium (multiple data mesh articles): https://medium.com/@p-platter

Agile Lab's website: https://www.agilelab.it/

Manisha's LinkedIn: https://www.linkedin.com/in/evermanisha/

'A streamlined developer experience in Data Mesh' blog post by Manisha: https://www.thoughtworks.com/insights/blog/data-strategy/dev-experience-data-mesh-platform

'A streamlined developer experience in Data Mesh (Pt. two)' blog post by Manisha: https://www.thoughtworks.com/insights/blog/data-strategy/dev-experience-data-mesh-product

'Data Mesh Accelerate Workshop' blog post by Thoughtworks: https://martinfowler.com/articles/data-mesh-accelerate-workshop.html

Max's LinkedIn: https://www.linkedin.com/in/max-schultze/

Max's Data Mesh Learning meetup presentation:

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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

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 / 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. You can download their Data Mesh for Dummies e-book (info gated) here.

Mike's LinkedIn: https://www.linkedin.com/in/2mikealvarez/

In this episode, Scott interviewed Mike Alvarez, Former VP of Digital Services leading the data mesh implementation at a large healthcare distribution company. He's now working on his own startup.

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

1. Lean in to the new value-creating possibilities that can come from empowering thousands of your colleagues to leverage data.
2. As an industry, we have to learn to do data work in an incremental fashion. It's not been the norm and it can break people's perception of data work but it's crucial to get where we want to go.
3. You can drive data mesh buy-in from domains by showing them the freedom they will have. Autonomy, empowerment, going at their own speed, etc. can get many to lean in.
4. Advice to past data mesh self: Early in your journey, you can share your vision until the cows come home and people will say they understand - and probably think they understand - but it's incredibly easy to get misaligned. Really focus on what you are trying to achieve. What are the target outcomes?
5. Similarly, it will be harder than you expect to drive buy-in. Many people say that but it's still going to probably be harder than you expect after hearing that :)
6. We need to move away from old approaches to data for large companies because the sheer scale of initiatives ends up creating bloat and risk factors unto themselves. Small and nimble gives us much quicker time to value delivery and builds to much greater outcomes.
7. Shadow IT develops to try to move at the speed of business for domains. But it's rarely scalable or robust enough to even support the domain in the...

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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

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 / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

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

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

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Sponsored by NextData, Zhamak's company that is helping ease data product creation.

For more great content from Zhamak, check out her book on data mesh, a book she collaborated on, her LinkedIn, and her Twitter.

Key Takeaways:

• Postel's Law: Be conservative in what you do, be liberal in what you accept from others.
• We can do better in data than what we did learning decentralization in services: "We have to level up. We can't repeat the past mistakes. Let's not be silly and fool ourselves just because we have a schema, now we have an amazing system."
• The services world has learned good ways of communicating between producers and consumers. We should look to learn more from them and look to adapt then adopt what works well.
• Zhamak believes we have to learn to prepare our data for future use cases. Scott note: If she means reuse of data being generated for current use cases, most agree. If she means creating data that doesn't currently serve a use case, almost everyone else seems to disagree. Time will tell.

More on Postel's Law: https://ardalis.com/postels-law-robustness-principle/

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

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

Data Mesh Radio episode list and links to all available episode transcripts here.

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.

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

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

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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

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 / 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. You can download their Data Mesh for Dummies e-book (info gated) here.

Himateja's LinkedIn: https://www.linkedin.com/in/himatejam/

Himateja's AWS ReInvent presentation (link starts at her part): https://youtu.be/y1p0BGsPxvw?t=1991

In this episode, Scott interviewed Himateja Mandala, Senior Data Engineering Manager and Head of the Data Mesh Data Platform at Disney Streaming. To be clear, she was only representing her own views on the episode.

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

1. ?Controversial?: Your existing data platform(s) might not be able to serve data mesh well, even with reasonable augmentation - especially if your data platform has become hard to change. You might have to build from scratch.
2. When the data platform's key users aren't part of the centralized team, you need to think about enabling automated capabilities by default, e.g. security the second data lands or easy to leverage and understand monitoring/observability.
3. ?Controversial?: Data products serving different use cases often end up looking relatively different. Is your data product for dashboards and reporting/analytics; is it for serving a recommendation engine or machine learning model; or is it more for internal usage? Be okay with data products not being uniform.
4. Even if your data mesh platform operates outside the traditional paradigms, many data producers - especially data engineers - will still be thinking data pipelines. Be prepared for that, it's an ingrained way of thinking for many.
5. Data contracts are very helpful in defining and maintaining quality. If you set up good observability on your data products, owners can quickly identify when there

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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

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 / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

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

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

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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

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 (most interviews from #32 on) here

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.

Important points to consider about being a data driven organization:

1. If your execs can't envision what would change if the organization were significantly better at data, you have some work on your hands at understanding their challenges first and then also evangelizing
2. Your domains should be leaning in and understand what being data driven means for their domain specifically. If they aren't you won't be able to really help them move forward. You can't drag a team to being data-driven.
3. Your execs should be aligned on what being data driven means for the organization, especially at the macro level - how does it all fit together instead of highly competent data silos? How do we focus on incremental value delivery via concrete use cases?
4. You should absolutely make sure your data strategy and your vision of the data driven organization ties to the actual business strategy and supports crucial priorities.
5. You need to make sure you have - or can build - a test and learn culture. If not, can you really be data driven?

Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him at community at datameshlearning.com or 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/

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