Data Mesh Radio Data as a Product Podcast Network

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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.
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:
"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 😅Focus on delivering value through data instead of delivering data and assuming it has value. – “Not all data is created equal.”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.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.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.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.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:
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.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.?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…Even aligning everyone on your organization's definition of mesh success will probably be hard. But it's important to do.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.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:
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 :)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.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.Data quality is NOT a project - it's a continuous process.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...

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.
Michael's LinkedIn: https://www.linkedin.com/in/mjtoland/
Marta's LinkedIn: https://www.linkedin.com/in/diazmarta/
Sadie's LinkedIn: https://www.linkedin.com/in/sadie-martin-06404125/
Sean's LinkedIn: https://www.linkedin.com/in/seangustafson/
The Magic of Platforms by Gregor Hohpe: https://platformengineering.org/talks-library/the-magic-of-platforms
Start with why -- how great leaders inspire action | Simon Sinek: https://www.youtube.com/watch?v=u4ZoJKF_VuA
In this episode, guest host Michael Toland Senior Product Manager at Pathfinder Product Labs/Testdouble and host of the upcoming Data Product Management in Action Podcast facilitated a discussion with Sadie Martin, Product Manager at Fivetran (guest of episode #64), Sean Gustafson, Director of Engineering - Data Platform at Delivery Hero (guest of episode #274), and Marta Diaz, Product Manager Data Platform at Adevinta Spain. As per usual, all guests were only reflecting their own views.

The topic for this panel was how to treat your data platform as a product. While many people in the data space are talking about data products, not nearly as many are treating the platform used for creating and managing those data products as a product itself. This is about moving beyond the IT services model for your data work. Platforms have life-cycles and need product management principles too! Also, in data mesh, it is crucial to understand that 'platform' can be plural, it doesn't have to be one monolithic platform, users don't care.

Scott note: As per usual, I...

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.
Carol's LinkedIn: https://www.linkedin.com/in/carol-assis/
Eduardo's LinkedIn: https://www.linkedin.com/in/eduardosan/
Continuous Integration book: https://www.amazon.com/Continuous-Integration-Improving-Software-Reducing/dp/0321336380
Measure What Matters book: https://www.amazon.com/Measure-What-Matters-Google-Foundation/dp/0525536221
Inspired by Marty Cagan: https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507
Empowered by Marty Cagan: https://www.amazon.com/EMPOWERED-Ordinary-Extraordinary-Products-Silicon/dp/111969129X
In this episode, Scott interviewed Carol Assis, Data Analyst/Data Product Manager and Eduardo Santos, Professor and Consultant, both at Thoughtworks. To be clear, they were only representing their own views on the episode.

From here forward in this write-up, I will be generally combining both Carol and Eduardo's views into one rather than trying to specifically call out who said which part.

Some key takeaways/thoughts from Eduardo and Carol's point of view:
At the end of the day, the team that produces the data will get the most use out of it 9/10 times. Getting teams used to developing with data in mind isn't just useful for the organization, it is for maximizing their own team's success.Continuous integration is a crucial concept in general for learning how to automate and focus on delivering more, which leads to...