Data Mesh Radio

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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.

Lynn's LinkedIn: https://www.linkedin.com/in/lnoel/

DAMA New England: https://damanewengland.org/

Correlation/Causation XKCD: https://xkcd.com/552/

In this episode, Scott interviewed Lynn Noel (pronounced Knoll), Data Governance Lead at AIM Consulting. To be clear, she was only representing her own views on the episode.

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

1. There is a strong data governance value proposition in data mesh around making data broadly "safer, better, and easier to use and share." Lean into that value and communicate it widely.
2. ?Controversial?: Many organizations doing data mesh are moving from a very centralized approach and they have certain concerns and approaches they should take. While other organizations are overly decentralized and need to be using different approaches to migrating. Really consider these as two different approaches that land on a similar end approach.
3. You should look to return governance to its original meaning: guiding and steering. In data mesh, governance shouldn't be about control.
4. ?Controversial?: "Not all governance is created equal." There are non-negotiables: infrastructure security and data classification. Don't over-index on sharing data at the expense of crucial governance.
5. Shift your governance left. Shift left the responsibilities - where appropriate - but also shift governance left in your timelines - it needs to be part of the build processes.
6. If you are doing data mesh, you have to understand 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/

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.

Eric's LinkedIn: https://www.linkedin.com/in/ericbroda/

Eric's Medium: https://medium.com/@ericbroda

Phill's LinkedIn: https://www.linkedin.com/in/redshirts/

Liz's LinkedIn: https://www.linkedin.com/in/elizabeth-negrotti-calloway/

In this episode, guest host Eric Broda, an Executive Consultant in the financial services space (guest of episode #38) facilitated a discussion with Liz Calloway, a Data Governance expert in Financial Services (guest of episode #92), and Phill Radley, Principal Data & AI Strategy Consultant at Thoughtworks. 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. This will be the standard for panels going forward.

Scott's Top Takeaways:

1. It’s important to understand that when you decentralize, different aspects can - and should - move at different paces. Your roadmap needs to account for that but it should also take advantage of that. Domains can go at their own pace, including ones looking to quickly drive towards significant data value.
2. Everyone's roadmap, by their inherent nature, will be unique based to the organization's context. Trying to copy-paste from another organization will end badly. That said, there are some pretty core capabilities that all mesh roadmaps should have.
3. A roadmap should point 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 (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.

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

'A streamlined developer experience in Data Mesh' articles by Manisha:

Part 1 - Platform: https://www.thoughtworks.com/insights/blog/data-strategy/dev-experience-data-mesh-platform

Part 2 - Product: https://www.thoughtworks.com/insights/blog/data-strategy/dev-experience-data-mesh-product

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

Blog post on mentioned data mesh workshops: https://martinfowler.com/articles/data-mesh-accelerate-workshop.html

In this episode, Scott interviewed Manisha Jain, Data Engineer at Thoughtworks.

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

1. Manisha's top 3 pieces of early mesh journey advice: A) put together a specification of what a data product is, make it clear. Your definition will evolve/improve but if people don't understand the building blocks, it's going to be hard to build value. B) start to create standardized input and output ports because that is how data products - your units of value - actually exchange value. C) make it easy to discover...

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 be better about getting on the same page regarding some semantics in data mesh. Otherwise, it's hard to work together internally and across organizations to move the industry forward.
• There are so many things we've learned about how to break systems into smaller components on the services side, about preventing tight coupling but the data world has yet to apply those learnings. We're heading down some paths that we don't need to if we follow past learnings.
• As an example of above, early data contract approaches are too tightly coupled around schema. We need to be a little less rigid there but how feels to be determined.
• Postel's Law: "Be conservative in what you do, be liberal in what you accept from others." Learn it and think about how to apply it to data so we create more resiliency across our internal data ecosystems. Right now, there isn't much out there on the how.
• Resiliency at scale is possible on the operational plane, why not the data plane? We need to be "very mindful and not naïve" around how we integrate in the data world to not make the same mistakes we made on the services side.

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

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,

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.

Beth's LinkedIn: https://www.linkedin.com/in/beth-bauer-102449/

Beth's Website: https://posiroi.com/

Harvard Business Review article on 'The 3 Elements of Trust': https://hbr.org/2019/02/the-3-elements-of-trust

In this episode, Scott interviewed Beth Bauer, Founder and CEO, PosiROI. FYI, there are lots of nuggets in this one for people creating a data strategy or trying to tie your data work to value creation.

Beth's ADEPT^2 Framework (covered briefly near the last 10min of the episode): Analytics - Acuity - Data - Decisions - Engagement - Enablement - People - Processes - Technology - Trust

Some key takeaways/thoughts from Beth's point of view, much of which she helped craft:

1. To do data right, we need shared responsibility. There is the technical piece of course but the business aspect is just as important. "…we need to realize that nobody's anything without each other" across the units and enterprise.
2. ?Controversial?: Really good data management can cause some challenges to power structures, especially "how it's always been done" power structures. Try to work with people to give them sight to how they are important in a changed organization.
3. ?Controversial?: Don't think data or digital _transformation_. A transformation is something that completes. This is a journey, an ever evolving journey of improving your data practices.
4. Data fluency is crucial - not just giving people the ability to work with data but the trust,...

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.

Here are 8 more leverage points to consider when trying to work with your initial domain

1. Make it EXTREMELY tied to their top priorities.
2. Find additional funding sources.
3. Find a true executive sponsor - think about being the one that brought a whole new approach that is driving far better data results…
4. Having their data be leveraged by other teams can mean more funding flows for their data projects and potentially their domain as a whole.
5. They control how things work for everyone in the data mesh implementation. They aren't just the trendsetter, the data mesh implementation leads will make it work for everyone but they are the secret favorite child and initial shaper.
6. Similar to moving at the speed of business, MUCH smaller experiments.
7. Better data quality.
8. Potentially easier regulatory reporting.

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/

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

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.

Guy's LinkedIn: https://www.linkedin.com/in/guytaylor/

Deep Work by Cal Newport: https://www.youtube.com/watch?v=xJYlhhT7hyE

In this episode, Scott interviewed Guy Taylor, Director of Data Science and Analytics, as well as the Director of Experimentation at Booking.com. To be clear, Guy was only representing his own views on the episode.

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

1. In general, "people want to do the right thing." Look to reward people that do the good, ethical things as part of their work product.
2. Always be asking "what is my part in this?" Ask for the expectations and try to be clear in your expectations of others.
3. Literacy is not only about an ability to read but also write. So with data literacy/fluency, we need to be able to use data but also create and share it. It's about learning how to share information - not just 1s and 0s - and share it well.
4. There are still major communication gaps between producers and consumers in many cases with data. Part of that is just not getting on the same page, really making sure both sides close that gap. Scott note: as Andrew Pease said, both parties should go more than half way to ensure you've covered everything.
5. If you don't align on expectations, you're far more likely to have a bad time :)
6. Data people need to stop trying to jump to the tooling to address a challenge first. Get the information necessary - what are people trying to accomplish to create value? - then look to how tools can drive towards capturing that value.
7. In tech and especially in data,...