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.
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...

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.
Jessika's LinkedIn: https://www.linkedin.com/in/jmilhomem/
In this episode, Scott interviewed Jessika Milhomem, Analytics Engineering Manager and Global Fraud Data Squad Leader at Nubank. To be clear, she was only representing her own views on the episode.
Some key takeaways/thoughts from Jessika's point of view:
There are no silver bullets in data. Be prepared to make trade-offs. And make non data folks understand that too!Far too often, people are looking only at a target end-result of leveraging data. Many execs aren't leaning in to how to actually work with the data, set themselves up to succeed through data. Data isn't a magic wand, it takes effort to drive results.Relatedly, there is a disconnect between the impact of bad quality data and what business partners need to do to ensure data is high enough quality for them.Poor data quality results in 4 potential issues that cost the company: regulatory violations/fines, higher operational costs, loss of revenue, and negative reputational impact.There's a real lack of understanding by the business execs of how the data work ties directly into their strategy and day-to-day. It's not integrated. Good data work isn't simply an output, it needs to be integrated into your general business initiatives.More business execs really need to embrace data as a product and data product thinking. Instead of a focus on only the short-term impact of data - typically answering a single question - how can we integrate data into our work to drive short, mid, and long-term value??Controversial?: In data mesh, within larger domains like Marketing or Credit Cards in a bank, it is absolutely okay to have a centralized data team rather than trying to have smaller data product teams in each subdomain. Scott note: this is actually a common pattern and seems to work well. Relatedly, the pattern of centralized data teams in the domains leads to easier compliance with regulators because there is one team focused on reporting one view instead of trying to have multiple teams contribute

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.
Marisa's LinkedIn: https://www.linkedin.com/in/marisafish/
Karolina's LinkedIn: https://www.linkedin.com/in/karolinastosio/
Tina's LinkedIn: https://www.linkedin.com/in/christina-albrecht-69a6833a/
Kinda's LinkedIn: https://www.linkedin.com/in/kindamaarry/
In this episode, guest host Marisa Fish (guest of episode #115), Senior Technical Architect at Salesforce facilitated a discussion with Kinda El Maarry, PhD, Director of Data Governance and Business Intelligence at Prima (guest of episode #246), Tina Albrecht, Senior Director Transformation at Exxeta (guest of episode #228), and Karolina Stosio, Senior Project Manager of AI at Munich Re. As per usual, all guests were only reflecting their own views.
The topic for this panel was understanding and leveraging the data value chain. This is a complicated but crucial topic as so many companies struggle to understand the collection + storage, processing, and then specifically usage of data to drive value. There is way too much focus on the processing as if upstream of processing isn't a crucial aspect and as if value just happens by creating high-quality data.

A note from Marisa: Our panel is comprised of a group of data professionals who study business, architecture, artificial intelligence, and data because we want to know how (direct) data adds value to the development of goods and services within a business; and how (indirect) data enables that development. Most importantly, we want to help stakeholders better understand why data is critical to their organization's business administration strategy and is a keystone in their value chain.

Also, we lost Karolina for a bit there towards the end due to a spotty internet connection.

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.
Darren's LinkedIn: https://www.linkedin.com/in/darrenjwoodagileheadofproduct/
Darren's Big Data LDN Presentation: https://youtu.be/vUjoJrl_MEs?si=WzB0sBStVIAyqDJs
In this episode, Scott interviewed Darren Wood, Head of Data Product Strategy at UK media and broadcast company ITV. To be clear, he was only representing his own views on the episode.
Scott note: I use "coalition of the willing" to refer to those willing to participate early in your data mesh implementation. I wasn't aware of the historical context here, especially when it came to being used in war, e.g. the Iraq war of the early 2000s. I apologize for using a phrase like this.

Some key takeaways/thoughts from Darren's point of view:
Overall, when thinking about moving to product thinking in data, it's as much about behavior change as action. You have to understand how humans react to change and support that. You can't expect change to happen overnight - patience, persistence, and empathy are all crucial aspects. Transformation takes time and teamwork.?Controversial?: In data mesh, it's crucial to think about flexibility and adaptability of your approach. Things will change, your understanding of how you deliver value will change. Your key targets will change. Be prepared or you will miss the main point of product thinking in data.When choosing your initial domains and use cases in data mesh, think about big picture benefits. You aren't looking for exact value measurements for return on investment but you also want to target a tangible impact, e.g. if we do X, we think we can increase Y part of the business revenue Z%.Zhamak defines a data product quite well in her book on data mesh. But data as a product is a much broader definition of bringing product management best practices to data. That's harder to define but quite important to get right.When thinking about product discovery - what do data consumers actually need...