Taking a needed break to focus on getting healthy. Be back in August!

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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.

Mirela's LinkedIn: https://www.linkedin.com/in/mirelanavodaru/

In this episode, Scott interviewed Mirela Navodaru, Enterprise and Solution Architect for Data, Analytics, and AI at Swisscom.

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

1. Specifically at Swisscom, it's not about doing data mesh. They want to make data a key part of all their major decisions - operational and strategic - and data mesh means they can put the data production and consumption in far more people's hands. Data mesh is a way to achieve their data goals, not the goal.
2. When you are trying to get people bought in to something like data mesh, you always have to consider what is in it for them. Yes, the overall organization benefiting is great but it’s not the best selling point 😅 try to develop your approach to truly benefit everyone.
3. Data literacy is crucial to getting the most value from data mesh. Data mesh is not about throwing away the important knowledge your data people have but it's about unlocking the value of the knowledge your business people have to be shared with the rest of the organization effectively, reliably, and scalably.
4. ?Controversial? You really have to talk to a lot of people early in your data mesh journey to discover the broader benefits to the organization. That way you can talk to people's specific challenges to get them bought in. When designing your journey, it is important to get input from a large number of people.
5. When talking data as a product versus data products, the first is the core concept and the second is the deliverables. Scott note: this is a really simple but powerful delineation
6. "No value, no party." If there isn't a value proposition, there shouldn't be any action. You need to stay focused on value because there are so many potential places to focus in a data mesh implementation.
7. You have to balance value at the use case level to the domain versus more global value to the organization. At the end of the day, everything you do should add value to the organization but sometimes use cases are...

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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.

Alyona's LinkedIn: https://www.linkedin.com/in/alyonagalyeva/

In this episode, Scott interviewed Alyona Galyeva, Principal Data Engineer at Thoughtworks. To be clear, she was only representing her own views on the episode.

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

1. ?Controversial? People keep coming up with simple phrasing and a few sentences about where to focus in data mesh. But if you're headed in the right direction, data mesh will be hard, it's a big change. You might want things to be simple but simplistic answers aren't really going to lead to lasting, high-value change to the way your org does data. Be prepared to put in the effort to make mesh a success at your organization, not a few magic answers.
2. !Controversial! Stop focusing so much on the data work as the point. It's a way to derive and deliver value but the data work isn't the value itself.
3. Relatedly, ask what are the key decisions people need to make and what is currently preventing them from making those decisions. Those are likely to be your best use cases.
4. When it comes to Zhamak's data mesh book, it needs to be used as a source of inspiration instead of trying to use it as a manual. Large concepts like data mesh cannot be copy/paste, they must be adapted to your organization.
5. It's really important to understand your internal data flows. Many people inside organizations - especially the data people - think they know the way data flows across the organization, especially for key use cases. But when you dig in, they don't. Those are some key places to deeply investigate first to add value.
6. On centralization versus decentralization, it's better to think of each decision as a slider rather than one or the other. You need to find your balances and also it's okay to take your time as you shift more towards decentralization for many aspects. Change management is best done incrementally.
7. ?Controversial? A major misunderstanding of data mesh that some long-time data people have is that it is just sticking a better self-serve consumption...

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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.

Arne's LinkedIn: https://www.linkedin.com/in/arnelaponin/

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

Foundations of Data Mesh O'Reilly Course: https://www.oreilly.com/videos/foundations-of-data/0636920971191/

Data Mesh Accelerate workshop article: https://martinfowler.com/articles/data-mesh-accelerate-workshop.html

In this episode, Scott interviewed Arne Lapõnin, Data Engineer and Chris Ford, Technology Director, both at Thoughtworks.

From here forward in this write-up, I am combining Chris and Arne's points of view rather than trying to specifically call out who said which part.

Some key takeaways/thoughts from Arne and Chris' point of view:

1. Before you start a data mesh journey, you need an idea of what you want to achieve, a bet you are making on what will drive value. It doesn't have to be all-encompassing but doing data mesh can't be the point, it's an approach for delivering on the point 😅
2. Relatedly, there should be a business aspiration for doing data mesh rather than simply a change to the way of doing data aspiration. What does doing data better mean for your organization? What does a "data mesh nirvana" look like for the organization? Work backwards from that to figure where to head with your journey.
3. A common early data mesh anti-pattern is trying to skip both ownership and data as a product. There are existing data assets that leverage spaghetti code and some just rename them to data products and pretend that's moved the needle.
4. "A data product is a data set + love." The real difference between a data product and a data set is that true ownership and care.
5. ?Controversial?: Another common mesh anti-pattern is trying to get too specific with definitions 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.

Saba's LinkedIn: https://www.linkedin.com/in/sabaishaq/

Decide Data website: ttps://www.decidedata.com/

In this episode, Scott interviewed Saba Ishaq, CEO and Founder of her own data as a service consultancy, Decide Data, which also provides 3rd party DAaaS (Data Analytics as a Service) solutions.

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

1. "If you don't know what you want, you're going to end up with a lot of what you don't want." This is especially true in collaborating with business stakeholders when it comes to data 😅
2. Focus on delivering value through data instead of delivering data and assuming it has value. – “Not all data is created equal.”
3. As a data leader, it's your role to help people figure out what they actually want by asking great questions and being a strong partner when it comes to the data/data work. Don't only focus on the data work itself but it's very easy to do data work for the sake of it instead of something that is valuable.
4. To deliver data work that actually moves the needle, we need to start from what are the key business processes and then understand the pain points and opportunities. Then, good data work is about how do we support and improve those business processes.
5. Relatedly, that's also the best way to drive exec alignment - talking about their business processes and how they can be improved first, data work second. They will feel seen and heard and are far more likely to lean in. At the end of the day addressing business and operational challenges is what data and analytics is all about.
6. Deliver something valuable early in any data collaboration with a business stakeholder. You don't have to deliver an entire completed project but time to first insight is time to value and you build momentum and credibility with that stakeholder.
7. At the beginning of a project - and delivering a data product is itself a project - you should work with stakeholders to not just define target outcomes...

Craziness of the overseas move (including a faulty office chair... long story) are to blame. Back to the normally scheduled one episode a week next week!

Episode list and links to all available episode transcripts here.

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.

Basten's LinkedIn: https://www.linkedin.com/in/basten-carmio-2585576/

In this episode, Scott interviewed Basten Carmio, Customer Delivery Architect of Data and Analytics at AWS Professional Services. To be clear, he was only representing his own views on the episode.

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

1. Your first use case - at the core - should A) deliver value in and of itself and B) improve your capabilities to deliver on incremental use cases. That's balancing value delivery, improving capabilities, and building momentum which are all key to a successful long-term mesh implementation.
2. When thinking about data mesh - or really any tech initiative - it's crucial to understand your starting state, not just your target end state. You need to adjust any approach to your realities and make incremental progress.
3. ?Controversial?: Relatedly, it's very important to define what success looks like. Doing data mesh cannot be the goal. You need to consider your maturity levels and where you want to focus and what will deliver value for your organization. That is different for each organization. Scott note: this shouldn't be controversial but many companies are not defining their mesh value bet…
4. Even aligning everyone on your organization's definition of mesh success will probably be hard. But it's important to do.
5. For a data mesh readiness assessment, consider where you can deliver incremental value and align it to your general business strategy. If you aren't ready to build incrementally, you aren't going to do well with data mesh.
6. A common value theme for data mesh implementations is easier collaboration across the organization through data; that leads to faster reactions to changes and opportunities in your markets. Mesh done well means it's far faster and easier for lines of business to collaborate with each other - especially in a reliable and scalable way - and there are far better standard rules/policies/ways of working around that collaboration. But organizations have to see value in that or there...

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.

Olga's LinkedIn: https://www.linkedin.com/in/olga-maydanchik-23b3508/

Walter Shewhart - Father of Statistical Quality Control: https://en.wikipedia.org/wiki/Walter_A._Shewhart

William Edwards Deming - Father of Quality Improvement/Control: https://en.wikipedia.org/wiki/W._Edwards_Deming

Larry English - Information Quality Pioneer: https://www.cdomagazine.tech/opinion-analysis/article_da6de4b6-7127-11eb-970e-6bb1aee7a52f.html

Tom Redman - 'The Data Doc': https://www.linkedin.com/in/tomredman/

In this episode, Scott interviewed Olga Maydanchik, an Information Management Practitioner, Educator, and Evangelist.

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

1. Learn your data quality history. There are people who have been fighting this good fight for 25+ years. Even for over a century if you look at statistical quality control. Don't needlessly reinvent some of it :)
2. Data literacy is a very important aspect of data quality. If people don't understand the costs of bad quality, they are far less likely to care about quality.
3. Data quality can be a tricky topic - if you let consumers know that the data quality isn't perfect, they can lose trust. But A) in general, that conversation is getting better/easier to have and B) we _have_ to be able to identify quality as a problem in order to fix it.
4. Data quality is NOT a project - it's a continuous process.
5. Even now, people are finding it hard to use the well-established data quality dimensions. It's a framework for considering/measuring/understanding data quality so it’s not very helpful to data...