Data Mesh Radio

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

Liz's LinkedIn: https://www.linkedin.com/in/lizhendersondata/

Liz's website: https://lizhendersondata.wordpress.com/

In this episode, Scott interviewed Liz Henderson AKA The Data Queen, Executive Advisor at Capgemini. To be clear, Liz was only representing her own views on the podcast.

Some high-level takeaways/thoughts from Liz's view:

1. To drive buy-in and engagement in a data strategy - especially with people outside the IT/data team - focus on the "why". Why are you doing this initiative or approach? What business goals is it supporting?
2. Also on driving buy-in for a data initiative, start by listening instead of selling/pitching. Focus on the business needs and work backwards to show how data can help address those needs.
3. To be successful with a large change-management data initiative, you need the patience, leadership, courage, and will to push forward. And the budget - don't forget the budget :D
4. You can't have an effective data strategy if it isn't directly tied into the business strategy. Really consider how data can help to support and execute on the business strategy. Data strategy in a vacuum away from the business is a recipe for trouble.
5. It's very easy to get overly focused in data on what you are delivering instead of why you are delivering it and who it is supposed to serve. If you want to be successful, you need to focus on the latter two. And look to deliver continuous incremental value rather than a back-end loaded value delivery.
6. Change management is very easy to get wrong in data. Really consider if you can not only get the ball rolling, but keep it rolling and in the right direction to implement a large change. Loss of momentum can mean loss of funding.
7. If data mesh follows a similar pattern to data literacy, it's likely to be 3-4 years from initial large swell in hype around data mesh - whether that was late 2021 or more now - until we really see a clear picture of how more organizations have implemented. There needs to be a time for trial and error.
8. Data literacy can only get you so far - you need data storytelling and...

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.

Karolina's LinkedIn: https://www.linkedin.com/in/karolina-henzel/

In this episode, Scott interviewed Karolina Henzel, Data Enablement Tribe Lead at T-Mobile Polska. FYI, businesses and domains are fundamentally similar in this conversation and are used essentially interchangeably.

Some high-level takeaways/thoughts/summarizations from Karolina's view:

1. Business transformation and impact is what really matters. Digital transformation is just a mechanism to transform your business into being more digital native and focused. Data transformation is just a part of digital transformation. Transformation should all come down to driving positive business impact.
2. To drive something like data mesh forward, you really need top management support, likely a C-level executive sponsor. Otherwise, it is very easy for work to get deprioritized and pushed out.
3. Don't take on a large-scale data initiative unless there are specific business challenges to address. Don't do data mesh for the sake of "being data driven"; what are the issues and why will addressing them help your business? Explicitly define the problems and the pain points.
4. To drive change, look for "change agents" in the domains. They are people with the will and capabilities to drive large-scale change. They aren't always easy to find but once you start to identify them, patterns will emerge.
5. The big pain points T-Mobile Polska was facing were: 1) poor/inconsistent data quality; 2) data discovery difficulties; and 3) slow time-to-market for new data and insights.
6. T-Mobile Polska was able to move forward with data mesh because business representatives in the domains were bought in that addressing the data pain points would drive incremental business value - there would be a return on the data work investment.
7. Look for quick wins and how to deliver continuous incremental value. If it is all about producing a big bang, you will very likely lose momentum, prioritization, and funding. Continuous value delivery is crucial to keeping people excited about data work.
8. T-Mobile Polska's data quality issues were caused mostly by a lack of accountability/ownership and not adhering to standard definitions across domains and...

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

Mahmoud's LinkedIn: https://www.linkedin.com/in/mahmoudyassin007/

In this episode, Scott interviewed Mahmoud Yassin, Lead Data Architect at ABN AMRO, a large bank headquartered in the Netherlands.

Some high-level takeaways/thoughts from Mahmoud's view:

1. It's very difficult to do fully decentralized MDM, which led to some duplication of effort - that can mean increased cost and people not using the best data. ABN tackled this through their Data Integration Access Layer - similar to a service bus.
2. They are using that centralized layer - called DIAL - to help teams manage integrations that are both consistently running and on-the-fly. It helps monitor for duplication of work instead of reuse.
3. If Mahmoud could do it again, he'd focus on enabling easy data integration earlier in their journey to encourage more data consumption. Cross domain and cross data product consumption is highly valuable.
4. The industry needs to develop more and better standards to enable easy data integration.
5. Data mesh and similar decentralized data approaches cannot fully decentralize everything. Look for places to centralize offerings in a platform or platform-like approach that can be leveraged by decentralized teams.
6. Most current data technology licensing models aren't well designed for or suited to doing decentralized data - it's easy to pay a lot if you aren't careful - or even if you are careful!
7. A tough but necessary mentality shift is not thinking about being "done" once data is delivered. That's data projects, not data as a product.
8. Try to keep as much work as possible within the domain boundary when doing data work. Of course, cross-domain communication is key but try to limit the actual work dependencies on other domains if possible.
9. A data marketplace enables organizations to more easily create a standardized experience across data products and make data discovery much easier. You don't necessarily have to tie your cost allocation models to the marketplace concept.
10. Sharing what analytical queries/data integration "recipes" people are using has been important for ABN. It drives insights across boundaries and also creates a lower bar to interesting tangential insight...

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

Stéphanie Bergamo

LinkedIn: https://www.linkedin.com/in/st%C3%A9phanie-baltus/

Twitter: @steph_baltus / https://twitter.com/steph_baltus

Simon Maurin

LinkedIn: https://www.linkedin.com/in/simon-maurin-369471b8/

Twitter: @MaurinSimon / https://twitter.com/MaurinSimon

In this episode, Scott interviewed Stéphanie Bergamo and Simon Maurin of Leboncoin. Stéphanie is a Lead Data Engineer and Simon is a Lead Architect at Leboncoin. From here on, S&S will refer to Stéphanie and Simon.

Some key takeaways/thoughts from Stéphanie and Simon's point of view:

1. "Bet on curious people", "just have people talk to each other", and "lower the cognitive costs of using the tooling" - if you can do that, you'll raise your chance of success with your data mesh implementation.
2. Leboncoin requires teams to share information on the enterprise service bus that might not be directly useful to the originating domain on the operational plane. They are using a similar approach with data for data mesh - sharing information that might not be useful directly to the originating domain by default.
3. Leboncoin presses teams to get data requests to other teams early so they can prioritize it. There isn't an expectation of producing new data very quickly after a new request, which is probably a healthy approach to data work/collaboration.
4. Embedding a data engineer into a domain doesn't make everything easy, it's not magic. Software engineers will still need a lot of training and help to really understand data engineering practices. Tooling and frameworks can only go so far. Be prepared for friction.
5. Similarly, getting data engineers to realize that data engineering is just software engineering but for data - and to actually treat it as such - might be even harder.
6. Software engineers generally don't know how to write good tests relative to data. Neither do data engineers. But testing is...

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.

Erik's LinkedIn: https://www.linkedin.com/in/erikherou/

H&M Career page: https://career.hm.com/

In this episode, Scott interviewed Erik Herou, Lead Engineer of the Data Platform at H&M. To be clear, Erik was only representing his own views and perspectives.

A few key thoughts/takeaways from Eric's point of view:

1. Data mesh can work well with a product-centric organization strategy as both look to put ownership and product thinking in the hands of the domains.
2. To develop a good data/enablement platform for data mesh, look to work with a number of different types of teams. That way, you can see the persistent/reusable patterns and capabilities to find ways to reduce friction for future data product development/deployment.
3. H&M had an existing cloud data lake that was/is working relatively well for existing use cases. But the team knew it likely wouldn't be able to handle where they wanted to go with many more teams producing data products of much higher quality and potentially sophistication.
4. When implementing data mesh - or any data initiative really - it is easy to fall into the trap of doing things the same way you did before. The "old way" feels safe and it was/is still working relatively well for H&M. So they treated their data mesh implementation as almost a greenfield deploy.
5. Because of the long-term focus on making it low friction and scalable to share data - the consumers will come as you make them more data literate - most of the early data/enablement platform work has been focused on helping data producers. A common pattern in data mesh but your constraints and needs may not match.
6. Erik's team is focused on enabling data producers first specifically so his team doesn't become a bottleneck. It is easy for a platform team doing any part of the individual work to become that bottleneck.
7. Consider how much organizational change you require before starting to create mesh data products. H&M did a large amount of that organizational change, other companies start in their current structure and evolve as they learn more. Both are valid and can work well.
8. Specific to...

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