Data Mesh Radio

IRM UK Conference, March 11-14: https://irmuk.co.uk/dgmdm-2024-2-2/ use code DM10 for a 10% off discount!

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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.

Ole's LinkedIn: https://www.linkedin.com/in/ole-olesen-bagneux-2b73449a/

Piethein's LinkedIn: https://www.linkedin.com/in/pietheinstrengholt/

Samia's LinkedIn: https://www.linkedin.com/in/samia-rahman-b7b65216/

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

Ole's book The Enterprise Data Catalog: https://www.oreilly.com/library/view/the-enterprise-data/9781492098706/

Piethein's book Data Management at Scale (2nd Edition): https://www.oreilly.com/library/view/data-management-at/9781098138851/

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

In this episode, guest host Ole Olesen-Bagneux, Chief Evangelist at Zeenea (guest of episode #82) facilitated a discussion with Piethein Strengholt, CDO at Microsoft Netherlands (guest of episode #20), Liz Henderson AKA The Data Queen, a board advisor, non-executive director, and mentor in digital and data at Capgemini (guest of episode #106), and Samia Rahman, Director of Enterprise Data Strategy, Architecture, and Governance at SeaGen/Pfizer (guest of episode #67). As per usual, all guests were only reflecting their own views.

The topic for this panel was modernizing master data management (MDM) and applying that to...

IRM UK Conference, March 11-14: https://irmuk.co.uk/dgmdm-2024-2-2/ use code DM10 for a 10% off discount!

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.

Learn more about Data Mesh Understanding: https://datameshunderstanding.com/about

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

IRM UK Conference, March 11-14: https://irmuk.co.uk/dgmdm-2024-2-2/ use code DM10 for a 10% off discount!

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.

Sue's LinkedIn: https://www.linkedin.com/in/suegeuens/

In this episode, Scott interviewed Sue Geuens, Director of Data Governance and Product Data at Elsevier. To be clear, she was only representing her own views on the episode.

We use the phrase MDM to mean master data management throughout the episode.

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

1. At the end of the day, if you want to do data governance well, it's about the people. Go talk to them, find out their specific needs and desires and work to tailor your language - and presumably your application of policies when possible - to their situations. People want good data, help them get there!
2. Relatedly, get good at telling stories about data work. Get people to lean in and get them involved. Personalize your communication!
3. While policies and standards are crucial, they are about creating better data for the organization. Try to leverage them as a carrot instead of a stick.
4. ?Controversial?: Don't talk about someone owning data. That's scary for most. Find ways to get them excited about owning the data without making it scary by using different phrasing.
5. The key to doing data governance well is getting people to care. We need them to care about the data because others have to use it. And that means the people are the most important focus.
6. Data governance is too focused on 'governance' and that means oversight. The word governance has a bad connotation for a reason - it makes many potential allies uncomfortable. So governance folks have to really work to make it less scary.
7. Don't focus so much on the data aspects of data work when talking with stakeholders. It's about achieving outcomes through data, not data work itself. Focus on what gets your business partners excited and that's (unfortunately) usually not...

Key points:

• Thus far, most of the generative AI stuff Zhamak has seen is not that much of a differentiator. They are doing far better chat bots but that hasn't really changed the game.
• When it comes to any ML work - and GenAI is just a subset of ML work - engineers need data products to make their data work easy. Reliable sources of data, ability to version, etc. Data mesh obviously plays well there.
• Relatedly, we need to continue to make things easier for people to leverage data products for GenAI. Engineers shouldn't have to spend all their time moving data around and using many systems.
• GenAI really could be game changing in data mesh but right now we don't have enough information to really do it well. We need far more metadata around things like data products.
• GenAI often gives extremely shallow answers that just aren't that helpful. If we can get better answers, amazing. But right now, it's not there.

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

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.

Frederik's LinkedIn: https://www.linkedin.com/in/frederikgnielsen/

In this episode, Scott interviewed Frederik Nielsen, Engineering Manager at Pandora (the jewelry one, not the music one 😅).

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

1. Your data technology and architecture choices incentivize certain behaviors. Consider what behaviors you want before you lock yourself in to anything.
2. Advice to past data mesh self: "construct a data architecture and platform that can adapt to the business requirements and wishes [which] will change over time." Build a composable platform as it's "easier to adapt to changing business requirements." Focus on decentralization features and make it decoupled and composable.
3. Trying to go too wide with your data mesh implementation at the start with all your domains makes it harder to really find your groove and build momentum.
4. Cost transparency can be a big driver for data mesh adoption. Teams want to understand their costs and many organizations are driving cost cutting initiatives. Decomposing the monolithic approach to data means better understanding the cost of individual pieces of data work.
5. Relatedly, when teams are responsible for their own costs, it's easier to spot when someone is making tradeoffs related to cost. It's a more tangible decision and can be a conscious decision to take on tech debt.
6. When taking a concept like data mesh to the highest levels in the organization, attach it to tangible use cases. Make it something that is worth their while, the 'juice must be worth the squeeze'. Focus on the strategic business goals and priorities.
7. It's okay to leverage management consultants. But your data ownership should very clearly be internal - external parties should not own any aspects if you want long-term success. Regarding consultants: "you would rather be driving them than them driving you."
8. It's absolutely normal for some teams to be more data mature than others. If teams raise their hands saying they need help with their data work, your culture is

Quick Summary Points

• Talk to the business strategy importance - data is there to make things better for the business. What could being better informed mean for your execs?
• When people ask about the strategy, that is when you can mention data mesh. It isn't about doing data mesh but you also aren't inventing this whole-cloth. 100s to 1000s of organizations are already on the journey. But data mesh is not some magic phrase, it is merely a framing for doing data better at scale.
• Think of the first hidden data demon from my upcoming mini-book: this is about getting to data driven, not being data dragged. This is about better equipping the people you thought were good enough to hire for their expertise and making them even better.
• Think of the second hidden data demon: data isn't only about strategic decisions - this gets us into a place where we can make better day-to-day execution decisions too.
• We don't get to skip leg day. I originally typed 'leg data' and maybe that's what we call the foundations 😅

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.

Mandeep's LinkedIn: https://www.linkedin.com/in/kaurmandeep80/

In this episode, Scott interviewed Mandeep Kaur, Enterprise Information Architect at Nordea Asset Management. To be clear, she was only representing her own views on the episode.

Nordea has been on their data mesh journey for a while and Mandeep has been trying to figure out best practices for the hundreds - thousands - of micro decisions in a journey. So how do we get comfortable with making so many calls?

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

1. "1) don't overthink it; 2) bring value out as soon as possible; [and] 3) evolution before completion."
2. The micro decisions in data mesh do matter, give them some thought. But it's important to simply get some perspective from the people who should know best and move forward. That can be from people inside or outside your organization but think about the blast radius of getting something wrong before you fix it. Most times it's smaller than you'd expect.
3. Your first question when considering data mesh: what value am I trying to get out of it? Think about what are the target value propositions and what does it do for the business if this is successful. If you don't have good answers, should you do data mesh?
4. The answers to the 'what value' question of your own mesh journey above should drive your strategy, where you should focus early and what will measure your success. And every organization will have different answers.
5. ?Controversial?: There's a LOT of overthinking in most data mesh implementations 😅 come back to your anchoring points around ownership/accountability, product thinking, value proposition, etc. What's important? You can try something and see if it works and change it if it doesn't, don't get caught in analysis paralysis.
6. Relatedly, always focus on the value proposition. If you are delivering value, you can improve the other aspects as you move along and learn to do aspects of your journey better.
7. There's a major challenge in abstract communication, especially about

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.

JGP's LinkedIn: https://www.linkedin.com/in/jgperrin/

Amy's LinkedIn: https://www.linkedin.com/in/amy-raygada/

Andrew's LinkedIn: https://www.linkedin.com/in/andrewrhysjones/

Andrew's website: https://andrew-jones.com/daily/

Andrew's book: https://data-contracts.com/

Data contract standard project Bitol: https://lfaidata.foundation/projects/bitol/

JGP's blog: https://jgp.ai/

In this episode, guest host Jean-Georges Perrin, Data Innovation Consultant at ProfitOptics (guest of episode #130 and panelist in episode #227), facilitated a discussion with Amy Raygada, Senior Data Product Manager at Swiss Marketplace Group (guest of episode #165), and Andrew Jones, Principal Engineer and Author of the book on Data Contracts (guest of episode #29). As per usual, all guests were only reflecting their own views.

The topic for this panel was all about data contracts and how do we go about getting them in place. Much of it was about the general concept but some of it was specifically about how do we think about data contracts applying to data mesh. This was the first topic I really did a deep dive into in early 2022 and it has evolved but is definitely still evolving.

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

Scott's Top Takeaways:

1. Data contracts are about trust and understanding. Trust that there is an owner and there are rules, there is a minder that knows this data matters. Trust that things...

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.

Alexandra's LinkedIn: https://www.linkedin.com/in/dralexdiem/

In this episode, Scott interviewed Alexandra Diem, PhD, Head of Cloud Analytics and MLOps at Norwegian insurance company Gjensidige.

Gjensidige's approach closely aligns with data mesh but they are starting with a focus on consumer-aligned data products as they have a well-functioning data warehouse and are not looking to replace what isn't broken.

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

1. Advice to past data mesh self: stop talking to people about data mesh, talk to the changes in the way of working. It can be very tiresome to try to explain data mesh instead of those changes. Data mesh isn't the point.
2. There aren't really any reasons we can't apply many software engineering best practices to data, it's simply we haven't done it broadly in the data world.
3. There is a push and pull between software best practices and data understanding. Consider which you see as more important and when. Do you bring data understanding to software engineers or software best practices to those with data understanding.
4. When you leverage pair programming between enablement software engineers and data analysts that understand the domain, the software engineers learn more about data and the domain and the analysts learn good software engineering/product practices. It's a win-win.
5. The people you enable to do work in a data mesh way should serve as ambassadors of your ways of working, especially within the domain. Both helping others learn and as champions. That provides organizational scale. You can't individually enable every person in a large company.
6. "Too many cooks spoil the broth." Think about having that 'two pizza team' kind of approach so you have concentrated understanding by those involved in creating data products who then can again help others learn. This is good for those in the domain and also for an enablement team bringing learnings back to a platform team.
7. Having a team with intimate knowledge of what data products/data product features have...