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

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 hereEpisode 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.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: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.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.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.?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.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"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.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 much more focused at the domain and that's perfectly expected and acceptable.Data mesh, to really change the organization in the right way, needs top level buy-in. You can't only be the data team trying to head down the data mesh path.Everything in data mesh is about iterating to better. You need the space and room to learn as you go along. You can - and must - deliver value before you've got everything figured out perfectly.Relatedly, you will learn how to better iterate towards value throughout your journey. It will be tough at the start as with any learning journey.Obviously, data mesh is a large cultural change. You need to have empathy and give people the chance to grow instead of trying to move too fast. Upskilling, especially around data literacy, is crucial.There are two very valuable aspects of data mesh: the value you deliver via use cases along the way and the value you get from learning to do data better across your organization. The first is from integrating data into far more of your decisions and the second means you can react more quickly to new opportunities and build scalable and reliable approaches to data management.Something like data mesh is a big change. But it shouldn't be a shock to people. You can do it gradually and incrementally while you deliver value. One of the best ways to lose people is to thrust disruptive change on them instead of working with them through the change to prevent large-scale negative disruptions.There are so many areas where data mesh helps organizations, whether it is getting away from silos, reducing redundancy, improving quality and reliability, etc. It's not just about doing data management itself better, which has been the focus of most data approaches historically.Again, data work is not the point. The point is to make your colleagues better at their job through being more informed. That comes down to the data but it's never the actual point, it's the vehicle to delivering value.Transparency and managing expectations - and communication in general - are crucial to doing data mesh well. You need to have that space to learn and iterate. Let people know what you are doing and especially why you are doing it.Data modeling in data mesh is of course a challenge. But it's important to have some level of common language between the domains or you will have data silos. It's a balance but it's crucial to give domains flexibility but also create easy paths for people to combine data across domains.Learn more about Data Mesh Understanding: https://datameshunderstanding.com/aboutData 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 hereAll 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

Interview with Alyona Galyeva, a Principal Data Engineer at Thoughtworks, on integrating technical and business perspectives for Data Mesh. Key points: data mesh is complex and requires effort, focus on deriving value from data, balance big-picture strategy with incremental improvements, challenges in implementation, and fostering collaboration for valuable data products.

Chris Ford and Arne Lapõnin share insights on starting a data mesh journey with clear goals for value creation. They discuss the importance of mindset, strategic buy-in, and stakeholder alignment. The podcast also covers challenges in balancing centralization and decentralization, navigating implementation obstacles, and considerations for internal and external stakeholders in data product development.

Saba Ishaq, an expert in delivering value through strong business collaboration, discusses the importance of focusing on value over data, asking great questions to determine needs, and aligning data work with business processes. The conversation emphasizes effective communication between technical and business teams, integrating data governance for measurable benefits, and delivering valuable outcomes through collaboration and understanding of business objectives.

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.

Scott interviews Basten Carmio, a Customer Delivery Architect of Data and Analytics at AWS Professional Services. They discuss the importance of finding a good initial data mesh use case that delivers value, improves capabilities, and builds momentum. Topics also include understanding starting states, aligning with business strategy, driving buy-in from stakeholders, delivering incremental value, and collaborating within a data mesh for increased business value.

Olga Maydanchik discusses 25+ years of data quality experience, emphasizing the importance of historical context and avoiding redundancy. Key points include data quality history, tools vs. human intervention, automated assessment tools, continuous improvement, and customer-supplier collaboration. The podcast also covers prioritizing data quality through education and incentives, highlighting the challenges companies face in ensuring high-quality data.

Data platform experts Sadie Martin, Marta Diaz, and Sean Gustafson discuss treating data platforms as products. They explore user-centricity, scalability, and aligning platforms with company culture. The shift from service-oriented to a product-focused mindset is emphasized, along with empowering engineers to build user-centric data platforms and the challenges of crafting a product vision for data platforms.

Carol Assis and Eduardo Santos from Thoughtworks discuss empowering development with actionable data, integrating data early in digital product development, evolving data management systems, managing cloud costs, navigating data ownership, balancing data insights with meaningful measurements, maximizing data impact with simple tools, and sharing their passion for data and poetry.