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 hereEpisode 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

Key Points:The rush to categorize all of our tooling in data has caused many issues - we will see a big shake-up coming in the future much like happened in application development tooling.So much of data people's time is spent on things that don't add value themselves, it's work that should be automated. We need to fix that so the data work is about delivering value.We can learn a lot from virtualization but data virtualization is not where things should go in general.Containerization is merely an implementation detail. Much like software developers don't really care much about process containers, the same will happen in data product containers - it's all about the experience and containers significantly improve the experience.The pendulum swung towards decoupled data tech instead of monolithic offerings with 'The Modern Data Stack' but most of the technologies were not that easy to stitch together. Going forward, we want to keep the decoupled strategy but we need a better way to integrate - APIs is how it worked in software, why not in data? Sponsored by NextData, Zhamak's company that is helping ease data product creation.For more great content from Zhamak, check out her book on data mesh, a book she collaborated on, her LinkedIn, and her Twitter. 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 hereData Mesh Radio 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

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.Contact email: Swimwith[at]gulpdata.comLauren's LinkedIn: https://www.linkedin.com/in/laurencascio/Chris' LinkedIn: https://www.linkedin.com/in/censey/In this episode, Scott interviewed Lauren Cascio, Chief Fish Wrangler, and Chris Ensey, CTO at Gulp Data.From here forward in this write-up, L&C will refer to the combination of Lauren and Chris rather than trying to specifically call out who said which part.Some key takeaways/thoughts from L&C's point of view:?Controversial?: Many organizations have an incorrect perspective that they mostly have a single type of data that's useful for each use case or need. Typically, their data is useful for many more internal use cases and also to organizations in far different industries.Often, there is a lack of a data sharing culture in many organizations. There isn't anyone that really understands how data flows throughout the organization or especially how it _could_ flow to serve many untapped use cases.There are many people emotionally attached to owning their own data but not in the product sense, they are focused on maintaining control rather than structuring it to be shared. So there are organizational challenges to data sharing in addition to technology.Many organizations have a tough time justifying updating their data infrastructure, leading to more and more challenges with progressing their data journey. It's often hard to point to a tangible ROI on updating the data platform for instance.Far too often, companies and LOBs know they want to analyze some information but they don't really know what they are analyzing it for. Instead of shaping data to make specific decisions, there is a focus on the visualization without a clear action in mind once the data tells them something. Drive towards what you care about and use data to answer those questions, the data doesn't speak for itself.Your upper management has limited patience and a limited attention span. Focus on what matters to them and be crisp on delivering an outcome with data, not outputs. A dashboard is just a pretty picture unless it drives action/creates insights.?Controversial?: It's often easier to get funding to prepare your data for external sale than investing in internal use cases. The simple reason is a tangible ROI. Look to frame your internal investments in data in the same way.Find ways to open more communication about what data you have internally. You will be surprised by the number of new use cases emerging. There's so much untapped data internally that people would use if they only knew about it and could easily use it.Many organizations aren't really thinking of the value of their data and how they protect it. If the data is so valuable to your organization, what kind of investment are you making in security and compliance to protect it?!Controversial!: People's personal data getting shared, at least with some modicum of regulatory oversight, is for the greater good - e.g. more patient data to help fight disease or more financial information to help unbanked people get access to credit/capital.If an organization wants to understand their overall data landscape, the best way to start is simply by starting and also having an end purpose in mind. Essentially, get conversations going and know why you are trying to understand your data. Is it to unlock new use cases, save costs, sell your data, etc.?L&C started with discussing how many organizations view their internal data landscape/estate and how it's not a complete picture. There tends to be a perspective that an organization's data is only useful for their internal use cases and often that each set of data is only useful for one type of use case. And L&C just haven't seen that be true - internally, most orgs have data that could be useful to existing use cases . How that typically manifests is data silos where data that should be shared isn't because people aren't aware it exists. Or the other side is that data producers have no real idea of how their data is being used downstream by other parts of the company. Externally, most companies' data is often very useful to other organizations in entirely different sectors.When asked about why lines of business have such a hard time understanding what data other LOBs have, L&C talked a bit about the technical challenges but much more about the organizational. In many - most? - organizations, lines of business have treated their internal data as overly precious, making sure it was structured specifically so they could use it. Trying to get them to structure it so others can use it is an emotional hurdle because it can feel like giving up that control. Thus, it's been hard for other LOBs to even know about the data across the organization, let alone use it. Add to that the challenge of businesses treating the data team and especially their infrastructure as a cost center and that further impedes their data journey. When it's hard to make the tangible business case for updating your data infrastructure, it's easy to fall further behind. If much of this sounds familiar, it's frequent hurdles towards implementing data mesh.L&C talked about how typical it is where organizations understand they want to visualize their data but without a specific goal in mind. Just visualizing without an expectation of what it will be used for is not product thinking. What information do you need to make your external facing products better? What will cause you to act? Instead, it's about "what does the data tell us" which is not often aligned with taking actual action. That leads to wasted cycles and money; it also often leads to wasted buy-in from upper management - they really only have limited patience, spend it on what matters. Be crisp on what goals you are going after then develop the data and analysis to help you actually go after those goals.It's far easier to get exec buy-in on selling your data externally than investing further for internal use in L&C's experience. That's because there is a tangible outcome at the end of the road. Look to try to shape your asks for additional funding based on that principle: a tangible ROI makes decisioning easier. For L&C, there are many use cases that could be unlocked in most organizations if only people knew what data was available. Finding ways to discover and share more about what data you have internally is very helpful. Yes, a data catalog is great but finding better ways to make people aware of the available data will unlock new valuable use cases. An audit for what data to sell externally is one way to spark these conversations but there are many others :)L&C pointed to two differing types of companies regarding selling their data. The first is low margin businesses. Because they are so reliant on volume, they end up with a considerable amount of data that they could potentially monetize. The other type of company is early stage companies that have yet to reach product market fit, especially B2B. They often think their data will be very valuable but selling data becomes a distraction far too easily. Focus on your core business, not small external monetization streams.On the somewhat controversial topic of data monetization, how people's information is protected versus leveraged, L&C believe there is a greater good in general to your information being shared. While something like GDPR gives the perception of your data being protected, it's not really all that true - everyone's data is out there already 😅. Meanwhile, there is lots of potential good that can come out of more comprehensive data sharing, e.g. better information to fight diseases from more patient information or lower cost of items in retail stores from data generated in loyalty programs. 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

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 hereEpisode 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

The podcast discusses the importance of repeatability in language to achieve scalability in data and software work. It highlights the challenges of varying user experiences in data products and the need for universal definitions. It also emphasizes the importance of universal standards for data quality and simplified definitions. The podcast encourages identifying and addressing unnecessary friction to improve data quality and adoption.

Stefan Zima, Data Transformation Lead at RBI, discusses sustainable data transformation and the challenges faced in implementing data mesh. He emphasizes the importance of communication, transparency, and sharing anti-patterns. The podcast explores the need for transformation, embracing change, defining data products, and compliance and governance challenges. It also highlights the importance of partnerships and finding your own path in data transformation.

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 hereEpisode 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

Data mesh implementation is discussed by expert guests Stefan Zima, Duncan Cooper, and Sid Shah. They talk about the leader's role, mindset shifts, hands-on experience, data evangelist role, measuring and balancing roles, importance of data logistics and coaches, reflecting on experiences, and knowledge sharing.

Iulia Varvara, Advisory Consultant in Digital and Organizational Transformation at Thoughtworks, discusses the importance of embracing product thinking in data for organizational transformation. She shares insights on mindset change, funding long-lived teams, and starting with a few domains owning their data. The podcast also explores aligning strategy and execution, implementing Data Mesh in organizations, and provides gratitude to listeners and information about the podcast and services.

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 hereEpisode 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