Data Mesh Radio

Key Points:

• The rush to categorize all of our tooling in data has caused many issues - we will see a big shake-up coming in the future much like happened in application development tooling.
• So much of data people's time is spent on things that don't add value themselves, it's work that should be automated. We need to fix that so the data work is about delivering value.
• We can learn a lot from virtualization but data virtualization is not where things should go in general.
• Containerization is merely an implementation detail. Much like software developers don't really care much about process containers, the same will happen in data product containers - it's all about the experience and containers significantly improve the experience.
• The pendulum swung towards decoupled data tech instead of monolithic offerings with 'The Modern Data Stack' but most of the technologies were not that easy to stitch together. Going forward, we want to keep the decoupled strategy but we need a better way to integrate - APIs is how it worked in software, why not in data?

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.

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

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

Please Rate and Review us on your podcast app of choice!

Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/

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. Get in touch with Scott on LinkedIn.

Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.

Contact email: Swimwith[at]gulpdata.com

Lauren's LinkedIn: https://www.linkedin.com/in/laurencascio/

Chris' LinkedIn: https://www.linkedin.com/in/censey/

In this episode, Scott interviewed Lauren Cascio, Chief Fish Wrangler, and Chris Ensey, CTO at Gulp Data.

From here forward in this write-up, L&C will refer to the combination of Lauren and Chris rather than trying to specifically call out who said which part.

Some key takeaways/thoughts from L&C's point of view:

1. ?Controversial?: Many organizations have an incorrect perspective that they mostly have a single type of data that's useful for each use case or need. Typically, their data is useful for many more internal use cases and also to organizations in far different industries.
2. Often, there is a lack of a data sharing culture in many organizations. There isn't anyone that really understands how data flows throughout the organization or especially how it _could_ flow to serve many untapped use cases.
3. There are many people emotionally attached to owning their own data but not in the product sense, they are focused on maintaining control rather than structuring it to be shared. So there are organizational challenges to data sharing in addition to technology.
4. Many organizations have a tough time justifying updating their data infrastructure, leading to more and more challenges with progressing their data journey. It's often hard to point to a tangible ROI on updating the data platform for instance.
5. Far too often, companies and LOBs know they want to analyze some information but they don't really know what they are analyzing it for. Instead of shaping data to make specific decisions, there is a focus on the visualization without a clear action in mind once the data tells them something. Drive towards what you care about and use data to answer those questions, the data doesn't...

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

Important points:

• There are places where nuance adds value. Many times, explicit definitions around data aspects like quality or even SRE metrics like uptime and query performance are not one.
• Provide a simple way for producers to apply these scalable approaches - the platform should measure data quality metrics for example.
• Data producers are having a hard enough time in general learning how to leverage data better. Find places to make it about learning about the information encapsulated in the data product, not learning a new set of SLAs for each data product.
• Consumers will thank you too since it make their lives easier. With that, you should see more of an uptick in data usage.

Please Rate and Review us on your podcast app of choice!

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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. 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

Please Rate and Review us on your podcast app of choice!

Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/

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. Get in touch with Scott on LinkedIn.

Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.

Stefan's LinkedIn: https://www.linkedin.com/in/stefan-zima-650229b7/

In this episode, Scott interviewed Stefan Zima, Data Transformation Lead at RBI (Raiffeisen Bank International AG). To be clear, he was only representing his own views on the episode.

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

1. No one has data mesh all figured out. Go talk to each other. But also don't be ashamed that you are running into challenges. So is everyone else. Data mesh implementers also need to share more of the anti-patterns they are finding.
2. Agile transformation really focuses a lot on communication and transparency. Both are very crucial to really any successful transformation initiative. Humans struggle with uncertainty and change so giving them a lot of information especially about the why prevents unnecessary pushback.
3. Relatedly, there are many things we can take from Agile transformation practices to apply to data/data mesh transformation. It's not a copy/paste but there's still much that is very relevant with some tweaks.
4. Many organizations are still focusing on technology-led transformation, whether data or digital in general. You must also change the mindset and organizational approaches if you want to be successful.
5. In banking, the rise of fintechs (financial technology companies) has made it clear that being nimble and quickly acting on data is crucial. Being data driven is required to remain competitive.
6. Data mesh can mean far less friction in getting to serving use cases. Instead of fighting against the data protection office, they are involved from the start. That time to market is especially crucial in banking now.
7. If you can, look to make your data sharing policies and approaches generic enough to only create friction when there truly is something different that should be examined further.
8. If you really want to be 'data-driven', if you really want to be a data company, you have to find and address the friction points in your data processes. Stop trying to simply get better at processes that...

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

Please Rate and Review us on your podcast app of choice!

Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/

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. Get in touch with Scott on LinkedIn.

Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.

Vanessa's LinkedIn: https://www.linkedin.com/in/vanessaeriksson/

Sid's LinkedIn: https://www.linkedin.com/in/siddharthin/

Stefan's LinkedIn: https://www.linkedin.com/in/stefan-zima-650229b7/

Duncan's LinkedIn: https://www.linkedin.com/in/duncan-cooper-1113722/

In this episode, guest host Vanessa Eriksson, the first CDO in Sweden and the head of data advisory company Vanessa Eriksson AB facilitated a discussion with Duncan Cooper, Chief Data Officer for Northern Trust Asset Servicing, Sid Shah, Head of Data Monetization and Platform at Airtel (guest of episode #258), and Stefan Zima, Data Transformation Lead at Raiffeisen Bank International AG (guest of episode #270). As per usual, all guests were only reflecting their own views.

The topic for this panel was about the leader's role in a data mesh implementation and what these four panelists have learned in that role. This was the second iteration of a panel we will likely have about every six months or so - the first was episode #215 from April of 2023.

Scott note: I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views individually.

Scott's Top Takeaways:

1. Regarding data mesh: get going but don't rush. Essentially, get started now but don't be in a hurry to try to get to some picture perfect end state. You need to take your time to make sure you are transforming instead of making changes that will unravel. Be brave and move forward into some uncertainty!
2. Relatedly, you will absolutely get many things "wrong" but wrong in a data mesh world can simply mean not right yet. We have ways to adapt/adjust and evolve as we learn and grow. Data mesh provides you the ability to iterate towards better constantly.
3. You _really_ should...

Please Rate and Review us on your podcast app of choice!

Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/

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. 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.

Iulia's LinkedIn: https://www.linkedin.com/in/iuliavarvara/

In this episode, Scott interviewed Iulia Varvara, Advisory Consultant in Digital and Organizational Transformation at Thoughtworks. To be clear, she was only representing her own views on the episode.

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

1. If you are greatly changing your general approach to something - which data mesh does in many ways - you need to focus some amount on actual transformation. These approaches are not a switch you flip, it takes time and concerted effort to make lasting changes that work well.
2. If an organization hasn't really broadly embraced product thinking, starting with data as a product/product thinking in data can act as a catalyst for other aspects of the business to embrace product thinking.
3. You don't change the organizational mindset through words - you start using new ways of working that change people's mindset as they see the benefit of those ways of working. At the end of the day, talk is cheap.
4. To do data mesh well and have it work for an organization, it's best to tailor to their existing ways of working. Yes, change is necessary but a revolution is far less likely to work than an evolution. How are teams working and where can we make smaller tweaks?
5. Because you need to tailor your implementation to your own organization, any data mesh blueprint that will supposedly work for all organizations is likely to be snake oil at best.
6. ?Controversial?: The first two principles of data mesh - domain data ownership and data as a product - have the most impact on the organizational...

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

Key Points:

• The current data product developer experience sucks. In software, we have one simple interface that manages all the pieces needed to get the job done, packages those components. In data, we have to jump through hoops and interfaces across many tools.
• Right now, the developer has to manage everything themselves which is a ton of work. But it's also a big risk because of lifecycle management - if everything isn't packaged and deployed as one, you have dependencies drifting from each other.
• There are many places from software we can learn from as to how to do this containerization. Ruby-on-Rails and CloudFoundry are good places to look.

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.

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

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