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.

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/elizabeth-negrotti-calloway/

Jay's LinkedIn: https://www.linkedin.com/in/jaycomoiii/

In this episode, Scott interviewed Jay Como, Head of Finance Data, and Elizabeth (Liz) Calloway, Director of Finance Data Products at Silicon Valley Bank. To be clear, they were only representing their own views and experiences.

Some key takeaways/thoughts from the conversation:

1. The governance team should "wear them down with empathy." Take the time to share your context, learn their context, make them feel seen and heard. That will get them to see you as a partner and good governance is truly about partnering, not mandating or being a gate/hurdle to get past.
2. Great governance is the pathway to great data. Great data leads to great decisions which lead to great outcomes. Share that path to great outcomes so people can see a clear answer to "why are we doing this?" Governance isn't just risk mitigation, it can be a significant - if almost always hidden/secret - value driver.
3. To drive governance buy-in from data producers, again, lead with empathy. Let them in on the "why" - why does this matter? What is the business value? How can this benefit them?
4. "Help me help you" is a good approach to talking to internal teams about data governance. You are there to drive value for them, take work off their plates when appropriate.
5. You can further drive buy-in through helping teams get to quick wins. While the long-term is obviously important, incremental value-add is better than a big bang approach.
6. Provide a constant stream of value, including executing on where you are helping, will drive teams to want to work with the central governance function. Drive value and the buy-in naturally comes with it.
7. Good data governance is necessary to avoid fees, fines, and the huge revenue/business impact bad data can have. But don't use those as a boogeyman, don't use fear to sell good data governance.
8. It's easy for a centralized governance team to become a bottleneck. Focus on not solving all the problems for teams but being there to help when...

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.

Björn's LinkedIn: https://www.linkedin.com/in/bjornsmedman/

In this episode, Scott interviewed Björn Smedman, Engineering Manager at Communication Platform-as-a-Service (CPaaS) company Sinch.

Some interesting thoughts or takeaways:

1. A good indicator for when decentralizing your data team might make sense is the cognitive load of a centralized data team. How many systems - including a measure of how complex - are they managing? How much of their time is spent in meetings, especially trying to understand context/requests? Is there starting to be combative prioritization from multiple domains?
2. It can be very beneficial and scalable to apply data mesh principles to non analytical use cases, especially sharing data for application purposes.
3. It is still often difficult to prioritize creating a data product for machine learning without knowing the business value of the ML model. But the ML team needs the data first before they can figure out the business value of the ML model. You have to make speculative bets.
4. If you see the data platform team start to dig into the semantics of a use case, that's a red flag that people are trying to leverage them as a data team. And while you want a centralized data platform team, you probably don't want them to become a centralized data team.

Since December 2020, Sinch raised nearly $2 billion USD. With this funding, they have made a number of sizeable acquisitions, with the company growing from 500 employees to over 3,000 in about a year. This has led to some interesting challenges in sharing data in a hyper-scaling environment.

Per Björn, data is a very key part of Sinch's plans for growth. Sinch's operational systems are often very transactional, as some product lines can process tens of thousands of monetary transactions a second, so data that might be typically shared on the operational plane in other companies is shared on the data plane lest the operational data stores deal with billions of events, making the data challenges even more complex than for most organizations. Then add in the regulatory requirements of telecom.

Björn helped lead the move to decentralizing the 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

Luca's LinkedIn: https://www.linkedin.com/in/paganelliluca/

In this episode, Scott interviewed Luca Paganelli, Data Architect at the Italian utility Gruppo HERA.

To start, some interesting points and/or key takeaways and questions:

1. Introducing new concepts and ways of working around data slowly - not looking to make a hard shift - has worked well. When Gruppo HERA debuted their new data strategy manifesto, none of it was a surprise and it was already relatively in-line with the way many were talking about data and moving forward on their data journeys.
2. HERA's Data, Analytics, and Intelligence Automation (DAIA) team is not forcing domains to comply with HERA's data mesh-inspired guidelines but instead working with them closely to help the domains achieve their data related goals - delivering the "right thing". That gives the DAIA team strong influence to direct the domains' approach to data work without pushback and gives domains better confidence in the guidelines and can mitigate analysis-paralysis risk. This lack of rigidity and strong rules created a better sociotechnical environment to innovate but it can mean nothing really feels standardized because the domains can still choose to go a different direction.
3. The paradigm-shift was initially "stee​p" for both IT and domain owners. But domain owners realized how much better they could serve themselves and external data consumers if they took over more data ownership. IT was afraid to give up control but started to buy in to the leverage and expertise they can provide by empowering the business domains to do great things with their data.
4. A concern with not having broad standardization is bespoke solutions so it is hard to create broad reuse. There is also a challenge of people not being sure how much they can trust the data products. The DAIA team believes the tradeoff is worth it to drive initial buy-in with domain owners.
5. Defining data products has been a struggle. There is a chicken and egg issue of 1) needing to understand who from the business should be involved in designing a data product but 2) data domains must be discovered to know who are the subject matter experts from the business to involve.
6. For HERA, they are...

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.

Links:

Joe's LinkedIn: https://www.linkedin.com/in/josephreis/

Ternary Data Website: https://www.ternarydata.com/

Monday Morning Data Chat: https://anchor.fm/ternary-data

Joe and Matthew Housley's interview with Zhamak Dehghani: https://www.linkedin.com/video/event/urn:li:ugcPost:6915063013410582528/

Joe's upcoming book, "Fundamentals of Data Engineering": https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/

In this episode, Scott interviewed Joe Reis, CEO/Co-Founder of data consultancy Ternary Data, Co-Host of the Monday Morning Data Chat, and author of the upcoming book Fundamentals of Data Engineering.

Some key points or takeaways specifically from Joe's point of view (not necessarily those of the podcast):

• Find quick, high-value wins. Too often people focus on the big wins and those become overly complicated and end up in failure.
• Most software engineers don't understand data well enough to be data product developers in data mesh, at least yet.
• Data mesh is a polarizing topic. And that makes sense as it is pushing boundaries. Many hope it can come to fruition but it is a bit of a utopian view.
• The future of data engineering is to move past managing pipelines to much higher-value work.
• Speed to achieving wins with data - with a clear return on investment and trust - is the first thing you should focus on. Get this right and you can have the "luxury" of building great data products.

Joe started by discussing the kind of nebulous area within software engineering and data that data engineering has always played - sit between the source systems and the data output, converting the data in the source systems into something consumable for data users. Previously, that was mostly about making...

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.

Relevant Links:

Jesse's Data Teams Book: https://www.amazon.com/Data-Teams-Management-Successful-Data-Focused-ebook/dp/B08JLFTPBV

Big Data Institute website: https://www.bigdatainstitute.io/

Data Dream Team podcast: https://sodapodcast.libsyn.com/site

Jesse's LinkedIn: https://www.linkedin.com/in/jessetanderson/

In this episode, Scott interviewed Jesse Anderson, Managing Director at consulting company Big Data Institute, host of the Data Dream Team podcast, and author of 3 books, most recently Data Teams.

To start, a few takeaways from Jesse's perspective on the choosing technology side:

• You should make sure you have the right team in place to make good technology decisions - the team needs to be in place first
• Before selecting any technology, it's crucial to understand what you are trying to accomplish. And to understand that the technology will provide help in addressing the challenge but won't solve anything itself
• Focus on: is this the right tool or solution for us now and in the future? What is the roadmap and vibrancy of the solution?
• "Technology must earn its keep", meaning you should understand the total cost of ownership and what is your expected return on investment
• Data tooling cycles are probably going to be 10 years at the most - prepare for obsolescence so you aren't overly reliant on any one technology

And some takeaways from Jesse's point of view on decentralizing data teams:

• Currently, software engineers aren't ready to be data product developers so you'd need embedded data engineers to handle creating and maintaining data products in data mesh
• But many data engineers are not willing to be embedded into domains
• Managing the dotted line versus solid line of reporting between a functional team and the domain is very difficult
• There are a number of cracks where crucial data can

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.

Immanuel's LinkedIn: https://www.linkedin.com/in/immanuel-schweizer-17839242/

In this episode, Scott interviewed Immanuel Schweizer, the Data Officer for EMD Electronics.

Some interesting thoughts and questions from the conversation:

1. Good governance starts at data collection - what are ethical and compliant ways to collect data from the beginning? This points to intentionality around data use stretching into the application - what should you collect that might not be part of the day-to-day application function but that might might lead to generating insights that will be used to generate a better user experience? And what are the ethical concerns?
2. Should we initially create data products to serve specific use cases or should we focus on sharing data first and then shaping what people consume most into data products? EMD is approaching data products from a different angle than most, using the second approach.
3. When looking at data mesh, should you start with the high data maturity teams or work to pull everyone up to at least a decent baseline maturity level? If you work with the most mature teams, will their challenges really be applicable to the not-so-mature domains? Can you find good reuse patterns to scale your mesh implementation?
4. Domain owners are much more willing to share data if they understand use cases for how their data will be used and maintain control to prevent misuse. Reluctance comes from an incomplete picture causing concerns - the more visibility into how data can be and is being used, the more willing domain owners are to share. But understanding your end-to-end data supply chain is tough, especially to start.
5. How do you evaluate when to spend the time with a domain to get them data mesh ready? If you need a high value use case to justify spending time with that domain, are you leaving many domains behind? This ties to #2 and #3.
6. Set your target picture but be ready to adjust your target picture along the way. The world is ever changing, don't lock in to an expected target outcome.
7. Good data governance is about speeding up 1) access to and 2) usage of data.
8. EMD launched a data literacy program where the employees spend the...