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 / 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.

LinkedIn: https://www.linkedin.com/in/brandonbeidel/

In this episode, Scott interviewed Brandon Beidel, Director of Product at Red Ventures.

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

1. Be willing to change your mind, especially based on new information. Be willing to measure and iterate. It's easy to get attached to tools or tech because they are cool. Don't! Stay objective.
2. It's crucial to align on what problem(s) you are trying to solve and why before moving forward on vendor/tool selection, no matter build versus buy. If it doesn't have a positive return on investment, why do the work?
3. Beware the sunk cost fallacy! It's easy to not want to shut something down that you've spent a lot on. But don't throw good money after bad.
4. When requirement gathering/negotiating, have a 'maniacal focus' on asking "what does this drive for the business?" You can quickly sort the nice-to-haves from the needs and you can have an open and honest conversation about cost/benefit of each aspect of a request.
5. When thinking about maximizing value, there is always one constraint that is the bottleneck. You can optimize other things but they won't drive the value. Find and fix the value bottleneck.
6. A simple two axes framework when thinking about use cases and requirements is value versus complexity. Look for high value low complexity first.
7. Be open and honest in discussions around expected costs of work/tools - which can be considered part of the complexity. The data consumers understand the value and can weigh the return on investment.
8. It's very important to understand data consumers' incentives so you can collaboratively figure out what is best for all...

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.

What can we do now relative to data mesh with what we have? People want to move, not wait for the tools to evolve. We can start to shift in anticipation of tooling getting better. It might not make things a ton better now, but when tools start to emerge, then we can jump ahead quickly. Learn from what happened in the API revolution and don't compromise on interoperability - that will just lead to high quality data silos, which is not a great outcome. And we need to get to a place with data where consumers have a delightful experience going from discover to learn to trust to use with little friction.

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.

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

Practical Data Privacy (Katharine's book in early release): https://www.oreilly.com/library/view/practical-data-privacy/9781098129453/

Katharine's newsletter: https://probablyprivate.com/

'Privacy-first data via data mesh' article by Katharine: https://www.thoughtworks.com/insights/articles/privacy-first-data-via-data-mesh

danah boyd [sic] website: https://www.danah.org/

In this episode, Scott interviewed Katharine Jarmul AKA K-Jams, Principal Data Scientist at Thoughtworks.

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

1. Increasing privacy around data does NOT mean you have to give up value.
2. Instead of data privacy being a blocker, it can turn nos to yeses because there is a better ability to restrict illegal/unethical use. Regulatory and legal people want to say yes, so give them the ability to do so.
3. There are lots of tools available to enhance your data privacy now. This isn't a pipe dream. That said, don't look to replace person-to-person conversations and decisions with tech. You'll learn when to use what on your journey, it's okay to iterate :)
4. Empower the people who know the data best with privacy tooling....

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.

Who will be the data product developer in data mesh? There has been a misconception in Zhamak's view that the application developers should be the ones focused on building the data products as well - but she thinks they already have a full-time role :) But, we need someone applying software engineering practices and data know-how to building data products. Right now, to do data work, you need way too much tool knowledge instead of data understanding. We have hyper specialized data roles - ML engineer, data engineer, etc. - when we should have data developers that can tackle these challenges better.

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.

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.

Mozhgan's LinkedIn: https://www.linkedin.com/in/tavakolifard/

In this episode, Scott interviewed Mozhgan Tavakolifard, Data and AI Lead for the Nordics at Accenture. To be clear, she was only representing her own views on the episode.

Before we jump in, most of the conversation was about external data marketplaces rather than internal data marketplaces within an organization. It's also important to note that data marketplace technology and implementations are still in the relatively early stages - it's quickly evolving and maturing.

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

1. Data marketplaces - internal and external marketplaces here - significantly lower the bar to data consumption because of standard metadata and user experiences. You should be able to easily see quality metrics, who owns a data product, access documentation, etc.
2. Data marketplaces, when done right, significantly lower the time to value realization for both data producers and consumers/purchasers. And standard quality measurements and metadata make it easy for consumers to understand how much they can trust data to make purchasing decisions easier.
3. Practices and tools are emerging for tracking data quality all the way to source to increase the trust data consumers/purchasers can put on data, especially for data marketplaces.
4. For external data marketplaces, trust and security are still major pain points. How can data producers trust consumers will protect the data they acquire and use it legally and ethically? What is their risk to consumers behaving improperly?
5. ?Controversial?: Mozhgan believes smart contracts and blockchain/distributed ledgers can provide for compliant use by...

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.

Mariana's LinkedIn: https://www.linkedin.com/in/mariana-hebborn-phd-118035117/

In this episode, Scott interviewed Mariana Hebborn, Lead of Data Governance for the Healthcare Sector at Merck Group Germany (not Merck, the pharmaceutical company).

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

1. It's crucial to answer why are you doing data governance. Is it for improving data quality? Better data security? Know what you are trying to achieve to best focus your efforts.
2. Make it easy for people to understand how and why to share their knowledge with the rest of the organization. The people mindset really is the most important aspect of successful digital and/or data transformation.
3. Most everyone knows we need to go to federated data governance but the big question is how. How can we do it safely? How can we evolve? It isn't a simple switch we can flip.
4. To drive buy-in for moving from a centralized data governance approach, we need to show the benefits of federated - when done well - versus a monolithic approach.
5. At the end of the day, governance is about conversation and missioning - why should you care about governance? What value will it drive for your organization? Answer those questions first.
6. We need to find ways to organize closer to the source to capture far more domain knowledge when sharing data. Centralized teams just can't understand the context in a large and complex organization.
7. ?Controversial?: Most data access should be to packaged insights - the computational result - rather than raw data itself. Most people consuming information want the insights, not the raw data.
8. We need to take learnings from operations

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.

What tech is already available that could be used for data mesh? There are so many amazing approaches and technologies in data but they've been used for the pipeline approach only. We need to think more like developers - not accepting the grunt work or death by a thousand cuts of data - and take a hard look at what we've done historically in data and what should be replaced.

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