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 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.

Gustavo Drachenberg's LinkedIn: https://www.linkedin.com/in/gusdrach/

Shawn Kyzer's LinkedIn: https://www.linkedin.com/in/shawn-kyzer-msit-mba-b5b8a4b/

Data Governance In Action: What Does Good Governance Look Like in Data Mesh - Interview w/ Shawn Kyzer and Gustavo Drachenberg

In this episode, Scott interviewed Shawn Kyzer, Principal Data Engineer, and Gustavo Drachenberg, Delivery Lead at Thoughtworks. Both have worked on multiple data mesh engagements including with Glovo starting 2+ years ago.

From here forward in this write-up, S&G will refer to Shawn and Gustavo rather than trying to specifically call out who said which part.

Some key takeaways/thoughts from Shawn and Gustavo's point of view:

1. It's very easy for centralized governance to become a bottleneck. Make sure any central governance team/board that is making decisions has a way to quickly work through backlog through good delegation. Not every decision needs deep scrutiny from top management.
2. To do federated governance right, you need to enable the enforcement - or often more appropriately the application - of policies through the platform wherever possible. Take the burden off the engineers to comply with your governance standards/requirements.
3. Domains should have the freedom to apply policies to their data products in a way that best benefits the data product consumers. So if there are data quality standard policies, the data product should adhere to the standard for measuring completeness as an aspect of data quality but might be optimized for something other than completeness.
4. The cost of getting anything "wrong" in data previously has been quite high because of how rigid things have been - the cost of change was high. But with data mesh, we are finding new ways to lower the cost of change. So it is okay to start with policies that aren't complete and will evolve as you move along.
5. If you have an existing centralized governance board, that will sometimes make moving to federated governance ... challenging at best ... so you will need a top-down mandate to...

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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.

Martina's LinkedIn: https://www.linkedin.com/in/martina-ivanicova/

In this episode, Scott interviewed Martina Ivaničová, Data Intelligence Engineering Manager at the travel services company Kiwi.com.

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

1. The most important - and possibly one of the most difficult - aspect of a data mesh implementation is "triggering organizational change".
2. Driving buy-in for something like data mesh is obviously not easy. As you are getting started, look to leverage 1:1 conversations to really share what you are trying to do and why and how this can impact them and the organization. These 1:1 conversations are crucial to developing early momentum.
3. On driving buy-in for data mesh, really think about how to limit incremental cognitive load as much as possible on developers/software engineers. If you can keep cognitive load low, you are much more likely to succeed - succeed in driving buy-in and succeed in delivering value.
4. When sharing internally about data mesh, it's important to focus on what it means to the other person. Using "data mesh" as a phrase can lead to a lot of confusion for people not on the data team. Make it clear what you are trying to accomplish - the what, the why, and the how. Using data-as-a-product as the leading concept resonated and worked well.
5. Kiwi.com started driving buy-in by working with the engineering upper management, then found a few valuable and achievable first use cases to move forward. And they have kept cognitive low on the engineering teams while they learn how to deliver data as a product.
6. If possible, the easiest way to drive buy-in is by finding a use case that is beneficial to the producing domain. If not, then look to spend the 1:1 time to really share why this matters.
7. Kiwi.com is getting software engineers in domains to commit to simply sharing their data, not even really structuring into data products. So the software engineers in most cases are really only focused on maintaining high-quality data sharing mechanisms - read: pipelines. That is a relatively low initial cognitive load/low workload ask.
8. Analytics engineers are creating the data products from the sourced data to...

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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 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.

Laura's book, Disrupting Data Governance: https://smile.amazon.com/Disrupting-Data-Governance-Call-Action/dp/1634626532

Laura's LinkedIn: https://www.linkedin.com/in/lauramadsen/

Moxy Analytics website: https://www.moxyanalytics.com/

In this episode, Scott interviewed Laura Madsen, CEO at Moxy Analytics and author of the book Disrupting Data Governance.

For the purposes of this write-up, when discussing data governance, it refers to the way many large organizations handle data governance at scale - a way that is very rigid and causes bottlenecks. We all know we can't stereotype or group every org together but general trends can be observed.

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

1. A big issue with today's data governance is that the concept of data stewards - the people who own the data concepts - is from 30 years ago and hasn't changed much despite the demands and scope changing dramatically.
2. The data governance committee/council structure most organizations use is inherently inflexible and ineffectual. Those making the decisions don't really understand what's happening with the data under the covers and those who do understand have little ability to influence the wider committee outside their own domain. And thus, they become a major bottleneck.
3. Data governance committees can be quite useful if they focus on communication and context exchange rather than driving decisions and work forward.
4. To drive change in your data governance practices, you need to disrupt but not destroy. Start to break down the big picture into much smaller, bite-sized chunks that when you improve on them will incrementally drive value - Agile provides a good framework to approach this.
5. You will absolutely have to throw out a LOT of your current data governance practices - over time - as you replace them with better ways of working. You will need to really evaluate each practice and assess if it will drive value or should be replaced.
6. "Marie Kondo" your data

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.

Anitha's LinkedIn: https://www.linkedin.com/in/anithajagadeesh/

In this episode, Scott interviewed Anitha Jagadeesh, Principal Enterprise Architect at ServiceNow. To be clear, she was only representing her own views on the episode.

Some key takeaways/thoughts from Anitha's point of view, some of which she specifically wrote:

1. It is absolutely crucial to tie the data strategy to the business strategy. The business strategy must drive the data strategy which drives your data architecture.
2. Architects need to lead the way in digging into use cases to get the specifics on what data producers are trying to solve for data consumers. Then, those architects can find the common patterns across use cases to tie to your organizational data strategy and also tie to your data architecture guidelines and principles. That way, instead of addressing challenges via point solutions, you can drive organization-wide choices that support many use cases via your data architecture.
3. Architects also need to ask the probing questions to continuously tie work back to the business strategy and value or expected outcome for customers. If you aren't driving the business strategy forward, if you aren't helping the big picture, is the work worth doing?
4. When it comes to data, companies shouldn't be entirely offensive - trying to leverage data for as much value as possible - or defensive - trying to minimize risk as much as possible. So organizations that have been very conservative need to push to be creative/offensive and high-risk organizations will get themselves into trouble if they don't start going defensive too.
5. As we build the data strategy we have to catalog our data assets/products and contracts to access these data assets/products – internal, external, and third party. Next steps we have to enable active metadata to ensure the catalog is always current.
6. Data contracts - especially SLAs and SLOs - are really crucial to driving reliable and scalable data practices forward. How can people trust what they are consuming without having to check it themselves unless there are very specific parameters and documentation of what they're getting? The data space needs to rework the way we approach data...

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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 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.

Gretchen's LinkedIn: https://www.linkedin.com/in/gretchenmoran/

NGS' current openings: https://ngs.wd1.myworkdayjobs.com/ngs_external_career_site

In this episode, Scott interviewed Gretchen Moran, the Senior Director, Data Products at the National Geographic Society (NGS; the non-profit arm of National Geographic).

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

1. NGS is a bit unique in that they don't have a widely deployed data architecture so they do not have a lot of habits to unlearn. Starting with a greenfield means likely more training and learning/experimenting will be required but at least no institutional unlearning.
2. To move forward with data mesh, organizations must be able to embrace change - and the pain that it will inevitably bring - and embrace ambiguity. You need to move forward and figure it out together but also be okay with failure as a learning experience as you test what works for your organization.
3. To win the hearts and minds of data producers, show them what high-quality data can mean for the organization and their domain/role. Work closely with them, understand their context, hold their hand to bring them along and align them to the vision of data mesh.
4. It's easier to drive buy-in widely if you find the organizational influencers and win them over. It is the domino effect in practice. Partner closely with the influencers early on to drive your initiative forward.
5. For NGS, they are working with a single initial data producing team for their proof of value. The data mesh world seems to be split a bit between working with one or two to three teams in the initial proof of value stage.
6. "Any technology effort is still a people effort."
7. We have yet to learn how to leverage the knowledge and context of people without data knowledge in general in the data and analytics space. This is what data mesh tries to unlock but we are still figuring out how to do it well.
8. It's very easy to intimidate people with data. We need to make tech and especially data much less intimidating to push broader adoption. The business context of