Data Mesh Radio

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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 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.

Akins' LinkedIn: https://www.linkedin.com/in/akinslawal/

Schema for Success: https://www.schemaforsuccess.com/about

In this episode, Scott interviewed Akins Lawal, a Data Strategist. To be clear, he was only representing his own views on the episode.

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

1. Far too often in data, people move to build _something_ instead of focusing on building good information architecture specific to the task at hand and the organizational goals and capabilities.
2. Good information architecture isn't about tech, it's about the principles and practices of how you're going to structure your data/information.
3. Keep going back to good product principles in information architecture: your focus should be on what are you trying to accomplish over what are you trying to build.
4. ?Controversial?: Organizations need to focus far more on hiring for learning capacity instead of only for current skills. The world is changing too quickly to try to focus on specific skills for many data-intensive jobs.
5. Leadership buy-in ends up being the number one determining factor of success for projects and transformation according to many studies. Trying to proceed - even with the greatest plan ever - without that buy-in greatly reduces the chances of success.
6. Maturity models can be extremely helpful but they sometimes don't tell the full story. Look for pockets of maturity in your organization and see what can be copied/replicated and what can't when improving the maturity 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

Key takeaways:

1. If you want to say you are doing data mesh, go ahead. If you really want to know if you are, look to Zhamak's book and her talks - how much are you attempting and are you going with the thin slice model - not ignoring any of the pillars. Have some empathy for yourself.
2. If others want to say they are doing data mesh, it's not really a big deal as long as we aren't trying to emulate the ones fooling themselves or over-marketing.
3. I'd rather welcome more folks into the tent than gatekeep. Maybe that is just my own prerogative but I have empathy for people who see what they think the end journey should look like. But it's like that transformation picture of build a skateboard, then bike, then moped, then motorcycle, then little 2 door car, etc. with the end idea of building a sedan.

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

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: 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 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

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 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.

Madhav's LinkedIn: https://www.linkedin.com/in/madhavsrinath/

In this episode, Scott interviewed Madhav Srinath, CEO at Nexusleap.

Overall, we are super early in the Generative AI cycle and hype is huge. This discussion is one of early impressions, not fully formed answers. It's far too early for that.

Also, FYI, there were some technical difficulties in this episode where the recording kept shutting down and had to be restarted. So thanks to Madhav for sticking through and hopefully it isn't too noticeable. Generative AI will mostly be shortened to GenAI throughout these notes. LLM stands for large language models which power GenAI.

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

1. ?Controversial?: An emerging best practice seems to be having layers of LLMs - one model where you might ask it complicated questions and the second model is trained specifically to vet the answers for correctness and governance concerns.
2. The cost of running many models in production is typically actually quite low, at least infrastructure wise. Instead of an always-on architecture, most organizations are leveraging a serverless architecture - or leverage APIs from others providing the models - so they essentially only pay a few cents per query.
3. ?Controversial?: Use GenAI as a "scalpel, not a broadsword". Many are trying to use them in overly broad ways and getting not great results.
4. The ability to take a mountain of data and get something out of it in a structured way isn't a new concept. We've been trying to do that with data mining for years. It's just that it is finally...

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

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 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.

Emily's LinkedIn: https://www.linkedin.com/in/emily-gorcenski-0a3830200/

Amy's LinkedIn: https://www.linkedin.com/in/amytobey/

Alex's LinkedIn: https://www.linkedin.com/in/alex-hidalgo-6823971b7/

Alex's Book Implementing Service Level Objectives: https://www.alex-hidalgo.com/the-slo-book

In this episode, guest host Emily Gorcenski, Head of Data and AI for Thoughtworks Europe (guest of episode #72) facilitated a discussion with Amy Tobey, Senior Principal Engineer at Equinix and Alex Hidalgo, Principal Reliability Advocate at Nobl9. As per usual, all guests were only reflecting their own views.

The topic for this panel was applying reliability engineering practices to data. This is different than engineering for data reliability which is focused on data quality specifically.

The overall concept is taking what we've learned from reliability engineering across disciplines but mostly in software, especially SRE/site reliability engineering, and bringing those learnings to data to make data - especially data production and serving - more reliable and scalable. Scott note: this is probably one of the most frustrating topics in data for me because it feels like it's basic foundational work yet most organizations aren't tackling this well yet if at all really. The best starting...

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 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.

Corrin's LinkedIn: https://www.linkedin.com/in/corrin/

In this episode, Scott interviewed Corrin Shlomo Goldenberg, Senior Product Manager of the Data Platform at BigPanda.

It's important to note that BigPanda is not at the stage yet where data mesh makes sense but this is a story of getting production of data into the heads and hearts of the application development team, which is a crucial aspect to doing data mesh well, whether it's done pre data mesh or as part of the journey.

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

1. When doing data work, it's easy to fall into the trap of trying to do everything. Go back to product basics - start from the why, why are you doing this? If not, we just are creating new forms data swamps.
2. It's not uncommon for developers to think of data simply as what's in the database, especially in B2B startups. Sometimes you have to work with them to get them to really understand they need to be creating and storing data to be leveraged for analytics. It's not even data exhaust, sometimes the data doesn't even exist!
3. Related, many B2B companies feel they aren't data oriented enough. You can work to change that of course but know that almost everyone else feels the same; we all start our data journey somewhere, get inspired to go forward.
4. It's hard to pinpoint the time for a growing B2B company when it's actually time to start collecting and analyzing a lot of their data versus when it would be overkill/too early. Scott note: for larger organizations, look to have the conversation early in the lifecycle of any product - build a data sourcing strategy...

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

Key Points:

• Containers in software abstracted away a number of very cumbersome tasks and encapsulated a lot of the dependencies software had to its environment. Combined, that meant developers could focus on delivering value instead of focusing on the infra. We need to do the same in data.
• It's all about sharing data in a responsible and easy way. That means putting all the components together so you don't have to manage many versions. Just like microservices.
• How do you manage to make this easy for the data product developer - bundle everything together. But centralized data products are creating a lot of potential issues/risk to scalability and flexibility. We just keep trying to centralize in data.

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.

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

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

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 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.

Jimmy's LinkedIn: https://www.linkedin.com/in/jimmy-kozlow-02863513/

In this episode, Scott interviewed Jimmy Kozlow, Data Mesh Enablement Lead at Northern Trust. To be clear, he was only representing his own views on the episode.

Also, FYI, there were some technical difficulties in this episode where the recording kept shutting down and had to be restarted. So thanks to Jimmy for sticking through and hopefully it isn't too noticeable that Scott had to ask questions without hearing the full answer to the previous question.

There is a lot of philosophical discussion in this conversation but tied to very deep implementation experience. It is hard to sum up in full without writing a small novel. Basically, this is one to probably listen to over just reading the notes.

Also, Scott came up with a terrible new phrase, asking people to "get out there and be funky."

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

1. Start your mesh implementation with your innovators. Find the people who are excited to try out something new. You want to spend your early time on innovating and learning, not constantly driving buy-in. Find good initial partners!
2. It's okay to start a bit simplistic - with each domain and on your general implementation - because you better believe there are complexities coming as you scale. This is not going to be simple. But the ability to tackle that complexity effectively is what differentiates data mesh so that unavoidable complexity ends up being where you find lots of incremental value.
3. Data product complexity is often from maintaining...