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.

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.

Ammara's LinkedIn: https://www.linkedin.com/in/ammara-gafoor/

Ammara's Articles on data mesh Part 1: https://www.thoughtworks.com/insights/articles/data-mesh-in-practice-getting-off-to-the-right-start

Part 2: https://www.thoughtworks.com/insights/articles/data-mesh-in-practice-organizational-operating-model

Part 3: https://www.thoughtworks.com/insights/articles/data-mesh-in-practice-product-thinking-and-development

Elif's LinkedIn: https://www.linkedin.com/in/elift/

AtScale article on data mesh: Principles of data mesh and how semantic layer brings it to life: https://www.atscale.com/resource/data-mesh-principles-semantic-layer/

General AtScale article: The Semantic Layer’s Critical Roles in Modern Data Architectures: https://www.atscale.com/resource/the-semantic-layers-critical-roles-in-modern-data-architectures/

Ryan's LinkedIn:

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.

Srinivas' LinkedIn: https://www.linkedin.com/in/spaluri/

In this episode, Scott interviewed Srinivas Paluri, CTO at Rentbase. Srinivas was previously part of a data mesh implementation as the Senior Director of Data Engineering at Zillow.

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

1. Data mesh advice to former self #1: Ambiguity is inevitable. Don’t be afraid of ambiguity - it's often better than binary thinking - but also be as clear as possible on responsibilities even if the target outcome is ambiguous. Clear responsibilities at least drive things forward.
2. Data mesh advice to former self #2: Involve product management way earlier. Every product owner needs to understand product ownership, prioritization, and what is the value to the business. And then communicate the value of the work to the business. If that value's not clear, why are you doing the work?
3. Data mesh advice to former self #3: Create a small team, maybe 5-6 people, focused on enabling new domains to learn how to own their own data and create data products. Scott note: see episode 48 for how ITV is implementing this pattern
4. Prioritization - and communication around prioritization - is probably one of your most useful tools in data mesh. If you get good at that, teams will often buy-in more quickly. Data producers see changing priorities, not more work. Consumers have a clear understanding of what work is done when and why instead of silence or a link to a Confluence or JIRA page.
5. Good data mesh product management isn't only focused at the data product level or the platform. You need to look at the bigger

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. People are seeing early value from data mesh but we can see where it could be a much greater amount but we are still held back by the organizational challenges and even more by the tooling.
2. In many cases, the tooling isn't good enough yet to change developer behavior - if we remove friction, they will likely want to lean in more on data mesh.
3. We have to find catalysts at the micro level in data mesh to make massive shift at the macro level. We can't try to change everything through pure force of organizational will. But we haven't found these catalysts yet.
4. It's easy to get lost in the vastness of change in a data transformation around data mesh. Try to focus more at the micro with a goal of creating cascading reactions to drive the macro.
5. We need to make mesh data products the first class primitives of information sharing - make them the basic building block of how we create our internal data/AI ecosystem.

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.

Ghada's LinkedIn: https://www.linkedin.com/in/ghada-richani/

In this episode, Scott interviewed Ghada Richani, Managing Director, Data, Analytics, and Technology Innovation at Bank of America. To be clear, Ghada was only representing her own views on the episode, not that of the company.

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

1. "What I know today is going to change tomorrow." Data mesh is a journey, don't try to get too comfortable, we should always be trying and iterating. Be an explorer. Scott note: strong agree and so does Zhamak
2. Speed is always a challenge with data mesh - some want to move too fast but others want to boil the ocean to make everything that comes after extremely fast. Work with people to take them along the journey and be part of the decisioning process, don't get ahead of yourself.
3. Take your stakeholders lockstep along your journey, keep them very informed. Let them control prioritization. That way, they can see what changes are happening and why delivery timelines are extending - they made those calls!
4. Expose the evolution of the data product to stakeholders. They can then understand tradeoffs, especially in regulatory or other governance challenges. And again, they own the prioritization :)
5. Don't start with generic requirements, start with stakeholder deliverables. The requirements will emerge from that conversation - stakeholders don't necessarily know what is required technically or as a product structure, but for sure they know what they want to achieve from a business perspective.
6. In data mesh, data producers should be treated as a stakeholder as well. Make sure they are engaged and that they are getting...

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.

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.

Stephen's LinkedIn: https://www.linkedin.com/in/galsworthy/

In this episode, Scott interviewed Stephen Galsworthy, former Head of Data at TomTom. Obviously, given he's no longer with the company, he was only representing his own views.

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

1. Just because the products you sell are made from data, that doesn't mean you necessarily have a good process for leveraging data to make your products better. You need to embed information collection into how you create products, how customers interact with your offerings.
2. The AI Flywheel: If you can get good information on product user experience, you can feed that into AI systems that generate incremental insights to improve your user experience even more. Hopefully, that generates more users which generate even more information to improve even further.
3. If data collection on usage is not part of your business model - for a number of reasons - it can be hard to convince customers and/or partners to enable that data collection. Even if it's simply to improve the user experience. Try to add it in to your product development as early as possible.
4. If your organization is data hesitant, look for existing success stories from data. Look for something that couldn't have happened without the data. And then share that success internally to drum up more interest.
5. ?Controversial?: Data should rarely be THE deciding factor. Data should be a touch point that can strongly inform or give clarity. Use it for giving clarity or measuring as you iterate. Help execs understand it's not magic and that it's not all right or wrong.
6. It's easy to trust data when it confirms your...

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.

Debra's LinkedIn: https://www.linkedin.com/in/privacyguru/

Debra's Shifting Privacy Left Podcast: https://shiftingprivacyleft.com/

Katharine's LinkedIn: https://www.linkedin.com/in/katharinejarmul/

Katharine's book: https://www.oreilly.com/library/view/practical-data-privacy/9781098129453/

Samia's LinkedIn: https://www.linkedin.com/in/samia-rahman-b7b65216/

Quick acronyms to know: PETs - privacy enhancing technologies; SMEs - subject matter experts

Scott Note Warning: there is some nerding out about how awesome it could be if some advanced privacy approaches and PETs were implemented at a broad scale across the industry to protect individual's privacy. It's pretty early days so warning about getting your hopes up :)

In this episode, guest host Debra Farber, privacy expert and host of the Shifting Privacy Left podcast facilitated a discussion with Katharine Jarmul, the author of the upcoming book Practical Data Privacy and Principal Data Scientist at Thoughtworks (guest of episode #157) and Samia Rahman, Director of Data and AI Strategy and Architecture at life sciences company Seagen (guest of episode #67).

Scott note: given this is a newer area, I wanted to share my takeaways rather than trying to reflect the nuance of the...