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.

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/

So here are the summation points of this episode:

1. Your legacy data platforms probably aren't going anywhere anytime soon. Those can have long lives but look to shut down unused capabilities
2. Your new projects, where applicable, should be data mesh data products. But a large percent aren't going to be that at the start. Figure out some incentives for people to push their new data products to your mesh platform early if possible
3. You should not look to lift and shift data projects/assets/products unless it is truly easy for all users - producers and consumers - lift and shift sounds great but it doesn't work well in all but the rarest of cases
4. Be prepared for people to get concerned about having to migrate early - communicate strongly that you aren't forcing migrations of existing data work

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.

Iryna's LinkedIn: https://www.linkedin.com/in/irinakukleva/

Mobey Forum: https://mobeyforum.org/

In this episode, Scott interviewed Iryna Arzner, Head of Group Customer Growth, Retail Banking at Raiffeisen Bank International (RBI). To be clear, she was only representing her own views on the episode.

Scott note: I mostly use the phrase line of business or LOB instead of domain in this write up but they are mostly interchangeable.

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

1. As a line of business head, data has value but only in so far as they can use it. If it's not aligned to a use case or business need, data work can be more of a distraction than a benefit.
2. It can be very interesting for a line of business owner to know how much their data is worth to other parts of the organization - that could drive funding for additional data work inside their LOB or even more funding than that because the LOB is core to driving business value at the organizational level.
3. "You cannot be successful in your data strategy if there are no business leaders that understand the value of the data and are very much determined to uncover this value." Scott note: couldn't put it better
4. A good way to get your business leaders more data fluent is to very closely pair with them. Sitting side-by-side on a project will up their fluency far better than any training course ever could.
5. "How do we get these data insights that we actually need to fuel the business strategy?" It's crucial to understand the LOB business strategy and focus data work around that. Start from the business needs...

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.

#233 Panel: A Head Data Architect's View of Data Mesh - Led by Khanh Chau w/ Balvinder Khurana, Yushin Son, and Carlos Saona

Khanh's LinkedIn: https://www.linkedin.com/in/khanhnchau/

Balvinder's LinkedIn: https://www.linkedin.com/in/balvinder-khurana/

Yushin's LinkedIn: https://www.linkedin.com/in/yushin-son-30362b1/

Carlos' LinkedIn: https://www.linkedin.com/in/carlos-saona-vazquez/

In this episode, guest host Khanh Chau, Director of Cloud Data Architecture at Grainger (guest of episode #44) facilitated a discussion with Balvinder Khurana, Technical Principal and Global Data Community Lead at Thoughtworks (guest of episode #135), Carlos Saona, Chief Architect at eDreams ODIGEO (guest of episode #150), and Yushin Son, Chief Architect of Data Platform & Data Products Engineering at JPMorgan Chase. As per usual, all guests were only reflecting their own views.

The topic for this panel was an architect's view of data mesh, especially from an architecture lead standpoint. There are many challenges architects face in data mesh, managing the micro level minutiae, down to the data product output and input port decisions but balance that with crucial high-level decisions. Balancing the near-term and long-term vision and roadmap/North Star.

Scott note: I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views...

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.

Aaron's LinkedIn: https://www.linkedin.com/in/aaron-wilkerson-81bb21a/

In this episode, Scott interviewed Aaron Wilkerson, Senior Manager of Data Strategy and Governance at Carhartt. To be clear, he was only representing his own views on the episode. Apologies for the lawn work sounds around the middle of the episode :)

Before we jump in, this episode contains a lot of really good framing on how data leaders can actually partner with business people to drive to what matters for them. How do you extract what matters to the organization and to each specific business partner? And then how do you tie the data work to that? So while this episode is not heavy on data mesh specifics, it's really important to really considering the business partner's point of view and how to work with them to drive value for the organization.

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

1. ?Controversial?: Aaron (and Scott) laid out a challenge for data leaders: have a conversation this week with a stakeholder and never mention data. We get too wrapped up in the data instead of listening and understanding stakeholder business challenges.
2. When thinking data strategy, you should first think business strategy. At the end of the day, it all comes down to how data can support the business in its objectives, not about doing data work for the sake of data work. What are the key business goals and target outcomes?
3. Business people very rarely care about the how of data, the sausage making. Don't try to communicate to them about the how, focus on the what and the why. Really drive towards what are they trying to accomplish and work backwards towards what data work you can do to...

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.

Takeaways:

1. We can do better in data than what we did learning decentralization in services/the operational side: "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."
2. 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.
3. We need to change our approach to measuring and reflecting on past decisions - we might have made a decision based on not great information, does that mean the decision was bad simply because it didn't work out? Probably not, but as Ari Gold said in Entourage "There are no asterisks in life, only scoreboards…" Can we really get to a place where we allow those asterisks?
4. Zhamak believes we can adopt many software development practices across data - that's pretty key to data mesh - but one area people seem to be skipping over are things like decision records - what were you thinking when you made a past decision, what did you know and what were your hypotheses. It's easy to judge results but it's better to judge the judgment :)

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

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.

Frannie's LinkedIn: https://www.linkedin.com/in/frannie-farnaz-h-a7a11014/

Post on the Product Trio concept by Teresa Torres: https://www.producttalk.org/2021/05/product-trio/

In this episode, Scott interviewed Frannie Helforoush, Technical Product Manager/Data Product Manager at RBC Global Asset Management. To be clear, she was only representing her own views on the episode.

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

1. There is a difference between the product mindset and creating/maintaining data products but both are very important to exchanging value through data. We should be looking to apply the product mindset to all aspects of our data work, not just how it applies to data products specifically.
2. To do data product management well, you should look to software product management practices and recontextualize those to data. Many map well but some don't. It's not a copy paste, think through what should be applied to data differently.
3. The data product manager needs to serve as the bridge between data producers and consumers, making sure consumer requirements are satisfied much like with a software product manager where they are the bridge between software engineering and software users.
4. If product management is the intersection of business, tech, and user experience (UX), how should we think about that for data product management? Tech and business are easy but there isn't a user interface (UI) so think of UX in terms of data fluency, access, and documentation.
5. Relatedly, documentation around data products is more important than...

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