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/

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.

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.

Kim's LinkedIn: https://www.linkedin.com/in/vtkthies/

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

Ferd's LinkedIn: https://www.linkedin.com/in/ferdscheepers/

Omar's LinkedIn: https://www.linkedin.com/in/kmaomar/

In this episode, guest host Kim Thies, Director of Intelligence Automation at PayPal facilitated a discussion with Ferd Scheepers, Chief Information Architect at ING, Mike Alvarez, Former VP of Digital Services at a large healthcare distribution company (guest of episode #236), and Omar Khawaja, Head of Business Intelligence at Roche (guest of episode #96). As per usual, all guests were only reflecting their own views.

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

Before we jump in, I think the main takeaway here would be a data mesh implementation leader's journey can be a lonely one. Find peers and exchange information. You can reach out to me (Scott) but there are also many leaders that want to exchange information with each other. The other is the meaning of journey: it's never done; be prepared to continue to push - it can feel Sisyphean but it's important to keep moving forward and expect to continue to drive buy-in.

Scott's Top Takeaways:

1. Everyone sees the 'Instagram photos' version of other organizations' data mesh journeys - it's not...

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.

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.

Marcie's LinkedIn: https://www.linkedin.com/in/marcie-stoetzel/

DGIQ Conference (Marcie and Samia Rahman will be speaking in June; in San Diego, CA, USA): https://dgiq2023west.dataversity.net/registration-welcome.cfm

Square hole video Scott mentioned: https://www.youtube.com/watch?v=cvwN_O5ypTE

Article about the "leaf sheep" sea slug Marcie mentioned: https://www.bbc.com/travel/article/20210324-the-odd-sea-creature-powered-by-the-sun

In this episode, Scott interviewed Marcie Stoetzel, Principle Product Manager of Enterprise Core Data at Seagen. To be clear, she was only representing her own views on the episode.

Before we jump in, Seagen might be a bit of a special case in how much the domains can leverage each other's data around a key record type. There is a lot to learn but you might not be able to find use cases that are as broadly impactful to many domains at once.

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

1. ?Controversial?: In data mesh, building a data culture focused on engagement, learning, and upskilling might be as important (more?) as doing the data work - if teams aren't willing to engage, what's the point of doing the work? Scott note: Marcie doesn't explicitly say this but it sure feels like an undercurrent. It's crucial to make your data culture something that can...

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:

• We need to make the data product the first class primitive of information sharing - make them the basic building block of how we create our internal data/AI ecosystem. (repeat from ZC20)
• "Tools reshape behavior" - if we want developers to change their relationship to data, we need to give them the capability to do so easily. Or even if not easily, at least not arduously :)
• The industry sees the value of data mesh but we need to find much lower effort ways to create sustainable, large-scale change. We all need to be finding catalysts.
• There is no reason to try to reinvent a lot of the technology in data at the physical layer. Lake storage, streaming technologies, ML libraries _at the physical layer_ are great. But we need new ways of accessing and leveraging them to make it far easier to create and manage data products.
• While everyone seems to be talking data products, there seems to be so many different definitions. This has led to a weaker market pull on vendors to improve their tooling to make data mesh more easily possible.

Semantic Diffusion article Zhamak mentioned: https://www.martinfowler.com/bliki/SemanticDiffusion.html

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,

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.

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.

Jyotshna's LinkedIn: https://www.linkedin.com/in/jyotshna-karki-81a24038/

In this episode, Scott interviewed Jyotshna Karki, Data Engineer at Novo Nordisk. To be clear, she was only representing her own views on the episode.

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

1. In data, especially in data engineering, people need to be curious. There are so many new innovations that may really be majorly beneficial. Look to try out more approaches and technologies.
2. You can have happy data producers and consumers with a centralized data lake setup and still have data mesh be the right evolution. In the long-run, at scale, it isn't efficient to have a centralized data team coordinating all data use cases.
3. For many domain teams, the centralized data processing and storage can be a black box. Data goes in, it gets transformed and stored by the central team and then served out. This can create a high dependency on experts and technology.
4. ?Controversial?: If your domain team consists of their own data engineers and data scientist with domain knowledge experts to manage their own data products, it's okay to work with multiple teams at the start of a mesh journey. Scott note: if you don't need to drive buy-in and your org can do this, I don't see it as a major risk. But probably at most a few hundred (tens maybe even) organizations are like this worldwide.
5. Don't try to enable every tool as part of your platform. You should focus and create a good experience on the most widely used tools rather than trying to support every tool available out there.
6. ?Controversial?: Probably don't try to automate processes at the proof of...

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

3 use case aspects or patterns to lean away from when trying to work with your initial domain:

1. What I call the domain type 'Everyone else in the organization wants your data so give it to us'.
2. Who is going to fund this? Not having a clear path to exec sponsorship and what the value expected will be is not great.
3. How do I add this to my priorities - essentially 'great, but that's more work I can't take on.'

And the 4 you should look to lean into:

1. Find your initial use case that is self-contained in the domain.
2. Move at the speed of business.
3. Find a use case that is easy enough with enough return to pay for itself as you also invest in the other aspects.
4. We will be investing heavily in making your domain better with data.

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.

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/

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.

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.

Chris' LinkedIn: https://www.linkedin.com/in/christopher-andr%C3%A9-haas/

Article on Lean Value Tree: https://rolandbutler.medium.com/what-is-the-lean-value-tree-e90d06328f09

In this episode, Scott interviewed Chris Haas, Advisory Consultant at Thoughtworks. To be clear, he was only representing his own views on the episode.

Some key takeaways/thoughts from Chris' point of view:

1. When starting a data mesh journey, you have to find teams that are actually willing to participate. If they won't truly take ownership of their data, it's essentially a non-starter. You can help convince many domains by showing them the investment you'll put into their teams on upskilling and other capabilities enhancements - it's not just new responsibilities and work.
2. In data mesh, look to drive domain-wide understanding and buy-in, at least to the vision of what a more data capable domain means, the benefits to the domain. Not everyone will or has to care about data mesh specifically but you shouldn't have data mesh by decree from upper management.
3. Consider lean value tree mapping - it helps everyone align on a single mission with goals to support the mission, what bets you have to make for each goal, and on down. It can help you stay focused on what are you trying to achieve.
4. For use case prioritization within a domain, look to 1) what are the business goals, 2) what use case will have the highest value, and 3) what is the easiest to execute on. Don't get overly focused on value if you aren't ready to actually deliver it....

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