Data Mesh Radio

Summer Hiatus Announcement - Back in August

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

#306 Building with People for People - Swisscom's Data Mesh Approach and Learnings - Interview w/ Mirela Navodaru

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/ [https://landing.datameshunderstanding.com/] If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here [https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing] Episode list and links to all available episode transcripts here [https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing]. Provided as a free resource by Data Mesh Understanding [https://datameshunderstanding.com/]. Get in touch with Scott on LinkedIn [https://www.linkedin.com/in/scotthirleman/]. Transcript for this episode (link [https://docs.google.com/document/d/1YGnfk8Z_U_9y2CrCYeBVV8C80tjr67FDFQCWNq6i704/edit?usp=sharing]) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here [https://www.starburst.io/info/data-products-for-dummies/] and their Data Mesh for Dummies e-book (info gated) here [https://starburst.io/info/data-mesh-for-dummies/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term=]. Mirela's LinkedIn: https://www.linkedin.com/in/mirelanavodaru/ [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 much more focused at the domain and that's perfectly expected and acceptable. 8. 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. 9. 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. 10. 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. 11. 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. 12. 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. 13. 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. 14. 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. 15. 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. 16. 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. 17. 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/about [https://datameshunderstanding.com/about] Data 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/ [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/ [https://datameshlearning.com/community/] If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here [https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing] All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm [https://pixabay.com/users/lesfm-22579021/], MondayHopes [https://pixabay.com/users/mondayhopes-22948862/?tab=audio], SergeQuadrado [https://pixabay.com/users/sergequadrado-24990007/], ItsWatR [https://pixabay.com/users/itswatr-12344345/], Lexin_Music [https://pixabay.com/users/lexin_music-28841948/], and/or nevesf [https://pixabay.com/users/nevesf-5724572/]

#305 Combining the Technical and Business Perspectives for Data Mesh - Interview w/ Alyona Galyeva

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/ [https://landing.datameshunderstanding.com/] If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here [https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing] Episode list and links to all available episode transcripts here [https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing]. Provided as a free resource by Data Mesh Understanding [https://datameshunderstanding.com/]. Get in touch with Scott on LinkedIn [https://www.linkedin.com/in/scotthirleman/]. Transcript for this episode (link [https://docs.google.com/document/d/1VhoXgm4n1ThIULVcAP-NvWr4drl04BHjsPtfJfsp0kc/edit?usp=sharing]) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here [https://www.starburst.io/info/data-products-for-dummies/] and their Data Mesh for Dummies e-book (info gated) here [https://starburst.io/info/data-mesh-for-dummies/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term=]. Alyona's LinkedIn: https://www.linkedin.com/in/alyonagalyeva/ [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 layer on top and we can continue to do monolithic data work under the hood. Be prepared for lots of friction in convincing some data architects that this isn't just a reskin or another layer on top of the enterprise warehouse or data lake. 8. For data mesh, it's crucial to understand necessary changes at the technical and the business level. You can't only work on one but you also don't have to 'solve' them at the start, make progress. It's like with the four principles, you need to thin slice change across the technical and business aspects rather than only focusing on one. 9. You can sell data engineers on data mesh by making their work more meaningful and impactful. Instead of mostly firefighting - which is the case in many organizations - they can focus on shipping new features and adding incremental value. 10. With data mesh, you want people focusing on more than just making data valuable - what is valuable will change so how do you make your data products evolvable and maintainable? 11. You always want to be focused on addressing people's pain points in data mesh, driving towards value. That's how you can get data people bought in as well, not just business people. 12. !Controversial! Doing aggregated data products across domains is usually the data mesh inflection point - basically answering can data mesh work in your organization. If you can't get that cross domain collaboration going well, you should consider another model like hub and spoke. 13. Relatedly, aggregating data across multiple domains is where there is usually the most value for an organization. But it's very hard to find good champions there because you need more vision and more hard work to collaborate across domain boundaries. Identify the people with the vision early in your journey, even if it's often better to actually only start working with them once you have more momentum and data products. 14. Too often, there is a rush to build _something_ instead of the right thing. Don't get fooled by the idea that data work always creates value. Even if the client or business partner asks you to build something of value, always circle back to the use cases. As much as we'd like to build universal data products, they just don't exist. 15. Relatedly though, don’t get so focused only on trying to build the consumer-aligned data products for hyper-specific use cases that you miss the forest for the trees. Sometimes the use case is something like 'we need to understand what data we even have to be able to use it to address our current problems in XYZ business line.' 16. To kind of sum it up: stop focusing on what you can build first. Focus on what you should build and then look at the realities. What matters to the business and why? Then focus on what's possible and what will deliver sustainable and maintainable value through data work. Learn more about Data Mesh Understanding: https://datameshunderstanding.com/about [https://datameshunderstanding.com/about] Data 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/ [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/ [https://datameshlearning.com/community/] If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here [https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing] All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm [https://pixabay.com/users/lesfm-22579021/], MondayHopes [https://pixabay.com/users/mondayhopes-22948862/?tab=audio], SergeQuadrado [https://pixabay.com/users/sergequadrado-24990007/], ItsWatR [https://pixabay.com/users/itswatr-12344345/], Lexin_Music [https://pixabay.com/users/lexin_music-28841948/], and/or nevesf [https://pixabay.com/users/nevesf-5724572/]

#304 Getting Your Data Mesh Journey Moving Forward - Interview w/ Chris Ford and Arne Lapõnin

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/ [https://landing.datameshunderstanding.com/] If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here [https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing] Episode list and links to all available episode transcripts here [https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing]. Provided as a free resource by Data Mesh Understanding [https://datameshunderstanding.com/]. Get in touch with Scott on LinkedIn [https://www.linkedin.com/in/scotthirleman/]. Transcript for this episode (link [https://docs.google.com/document/d/1QJ5aA9in_ru75PuXWn8QV8zlIyLA6LB2UA1QJZkTeVc/edit?usp=sharing]) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here [https://www.starburst.io/info/data-products-for-dummies/] and their Data Mesh for Dummies e-book (info gated) here [https://starburst.io/info/data-mesh-for-dummies/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term=]. Arne's LinkedIn: https://www.linkedin.com/in/arnelaponin/ [https://www.linkedin.com/in/arnelaponin/] Chris' LinkedIn: https://www.linkedin.com/in/ctford/ [https://www.linkedin.com/in/ctford/] Foundations of Data Mesh O'Reilly Course: https://www.oreilly.com/videos/foundations-of-data/0636920971191/ [https://www.oreilly.com/videos/foundations-of-data/0636920971191/] Data Mesh Accelerate workshop article: https://martinfowler.com/articles/data-mesh-accelerate-workshop.html [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 prescriptive advice. There isn't a copy/paste approach that will work and getting a specific definition of a data product doesn't really change much. Mindset is far more important than definitions. 6. It can be very helpful to have some simple checklists around your data products. While there is no prescriptive way to build, checklists remove a lot of the uncertainty for teams asking 'am I doing this right?' It gives some simple reassurances that you aren't missing out on key pieces of what they're building. 7. ?Controversial?: Most organizations probably don't need to do a ton of pre-work before starting on a data mesh implementation. They need some achievable goals, a roadmap for how they plan to achieve those goals, and a lot of willpower to push things forward and keep going when the going gets tough. You also need an enticing vision for people to buy into. 8. THIN SLICE! Don't try to take everything on at once but also don't try to skip over any of the four pillars. There's a reason they haven't changed from Zhamak's initial blog post. Scott note: don't try to argue the governance pillar wasn't in the first blog post, it just wasn't called out separately… 9. Three key questions to answer if you are considering data mesh: A) Do you have sufficient scale? B) Do you have a strategy that depends on deriving value from data? C) Are you prepared to take advantage of the autonomy Data Mesh will afford to your product teams? If you don't have satisfactory answers to those three questions, data mesh is probably not right/overkill for you. 10. If people don't see the strong need to transform your business through data, it's likely to lead to troubles 6-9 months into your data mesh journey. If you aren't addressing key organizational pain points or delivering value, you will likely lose support for your data mesh implementation initiative. Doing data better has to be valued to get more budget to keep going. 11. Another anti-pattern is focusing too much on use cases at the expense of the platform and the journey. Data mesh is designed to work at scale and that only works by finding repeatable processes. You can't treat each data product like a one-off. 12. In order to get buy-in from the data engineers - or whoever are your data product developers - you need to invest in changing hearts and minds through the platform. If creating and managing data products is significantly harder than the old way of dealing with data, you will lose people quickly. 13. Read about the data mesh accelerate workshop 😅 14. When you think about first steps with data mesh, A) build buy-in at the strategic level that you want to actually start leveraging your data for high-value purposes; B) find use cases to support those strategic initiatives; and C) make sure you are ready to actually thin slice and not try to only tackle on pillar - you have to be ready to take on a LOT of challenging work. 15. !Controversial!: None of the four data mesh principles are all that useful on their own. Scott note: there's a figure Zhamak has that explains why all four are necessary in conjunction that's very helpful here. 16. It's easy to want to skip bringing all your key stakeholders into alignment early in your data mesh journey. But you need matching expectations and shared understandings of what you are trying to accomplish and why. Scott note: this doesn't mean everyone has to be bought in that they are first, there's a balance to be found here. 17. You need room to make mistakes and adjust your data mesh implementation because you will not get it all right at the start. Data mesh is as much about learning how to do data well as doing data well. 18. It's crucial to not just ask if you are succeeding with your data mesh implementation but measure that. It can be hard to measure but consider what matters to your implementation's success and find things to measure if you're succeeding in each of those areas. Otherwise, how do you know where to focus and optimize? 19. Subsidiarity: "everything should be decided as locally as possibly but no more so." Basically, there are many decisions that should be made in the domains but there are some that need to be made centrally. The challenge is figuring out which decisions should be made where 😅 20. There will be capability challenges in every organization when doing data mesh. That will impact initial decisions around how much to centralize or decentralize but as you upskill the teams, you may want to decentralize more. Find your equilibriums but equilibriums change. It's all about trade-offs! 21. Many people are too focused on exactly if they are doing data mesh instead of are they delivering value in a scalable way through data. That happened in microservices too and it took them 10+ years to really get to best practices. Data mesh is only 5 years old and only ~3 with any number of organizations attempting it - focus on getting better instead of being worried if you're the perfect picture of data mesh yet. 22. When talking about your data mesh success internally, you need to talk about the value from use cases AND the value of improving your data capabilities in general. You prove out you are delivering specific value along the way but also that you are getting more and more capable at doing the data work to make the organization better. Both are of valuable and you should promote the value of both aspects: use case value and capability value. 23. When talking about data mesh, use the ADKAR method: create Awareness and Desire, give them the Knowledge about how you're doing it, upskill people so they have the Ability to do data mesh, and finally constantly Reinforce the value and that it's important. Without touting your mesh successes, you'll lose momentum. 24. When looking for your first data mesh use case, look for something that has a customer impact - what can you do for them that you couldn't before. Personalization is a good example. Legal is potentially another place re reducing risk. Learn more about Data Mesh Understanding: https://datameshunderstanding.com/about [https://datameshunderstanding.com/about] Data 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/ [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/ [https://datameshlearning.com/community/] If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here [https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing] All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm [https://pixabay.com/users/lesfm-22579021/], MondayHopes [https://pixabay.com/users/mondayhopes-22948862/?tab=audio], SergeQuadrado [https://pixabay.com/users/sergequadrado-24990007/], ItsWatR [https://pixabay.com/users/itswatr-12344345/], Lexin_Music [https://pixabay.com/users/lexin_music-28841948/], and/or nevesf [https://pixabay.com/users/nevesf-5724572/]

#303 Delivering What Matters - Value - Through Strong Business Collaboration - Interview w/ Saba Ishaq

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/ [https://landing.datameshunderstanding.com/] If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here [https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing] Episode list and links to all available episode transcripts here [https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing]. Provided as a free resource by Data Mesh Understanding [https://datameshunderstanding.com/]. Get in touch with Scott on LinkedIn [https://www.linkedin.com/in/scotthirleman/]. Transcript for this episode (link) [https://docs.google.com/document/d/1UWpEgpvL3nnDDioxrij6cXegnRQRCU-Sto8xE-L5euQ/edit?usp=drivesdk] provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here [https://www.starburst.io/info/data-products-for-dummies/] and their Data Mesh for Dummies e-book (info gated) here [https://starburst.io/info/data-mesh-for-dummies/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term=]. Saba's LinkedIn: https://www.linkedin.com/in/sabaishaq/ [https://www.linkedin.com/in/sabaishaq/] Decide Data website: ttps://www.decidedata.com/ [https://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 but also define how are you going to collaborate and communicate. You can't just get requirements, go away and build. Working with data should be iterative and should have an element of continuous improvement to evolve what you deliver as you build value. 8. Start any data work by asking someone about their business objectives, challenges, and target outcomes. You need your business stakeholders to have a clear vision of what they want to achieve, otherwise you are likely to be delivering only data work instead of business value that leverages your data work. 9. By doing deep discovery work, you can find where are the key lynchpins and value drivers in a use case. There are points of criticality that are easy to lose in a sea of potential requirements that are really requests. Find those crucial value leverage points! 10. Relatedly, you can use those value leverage points to keep your business execs engaged. They will - hopefully - see the importance and help you narrow in on what matters in their use case. Then it's no longer about the data work but the value to them. 11. ?Controversial?: For data people, you have to balance career management and interesting project/technology work versus value delivery. That doesn't mean delivering value isn't interesting but it doesn't always mean getting to play with the latest and greatest. But if data people never get to have fun and play with cool tech, many will leave. It's a tough balance. Try to make the valuable work also interesting 😅. 12. Relatedly, try understanding the data team’s learning areas of interest and see how you can build seeds to foster their skill growth while making data work valuable. Sometimes it turns out to be a win-win situation. 13. Relatedly, be very transparent and communicate a lot to your data teams about what you are prioritizing and why. It's very easy to get lost in telling data people to do certain work rather than why they are doing that work. Keeping your data people in the loop of the why will keep them focused on what matters. 14. For many organizations, the rate of change of their technology - application and data technologies - is growing at faster than the rate of their people change management/transformation processes. You need parallel streams to modernize both or your people will fall further behind, leading to chaos. 15. ?Controversial?: Relatedly, your overall org and/or digital transformation strategy should be tied to your data strategy. Otherwise, they will likely be heading in different directions, creating more challenges. Scott note: Benny Benford talked a lot about this in episode #244, going far together. 16. Data management is a very crucial element of digital transformation but it’s not the same thing as change management. The data team shouldn't be the ones leading the overall digital transformation of the organization. That's too much on a team that specializes in data rather than change management. If you are in that situation, it's a very tough spot to do well. 17. It's very important to focus on communication to stakeholders when you think about data governance and digital transformation. For many execs, these are foreign topics so you have to work hard to engage them and keep them leaning forward on the necessary work. Data governance is beneficial for everyone, so if explained and defined well people will engage willingly after knowing what’s in it for them. 18. As someone in the data team, you have to be well informed about digital transformation initiatives inside your organization. Otherwise, you will miss opportunities to align to those initiatives AND have all your data sources break when there is a migration you weren't told about 😅 19. It's easy to screw up the data steward/ownership conversation letting someone know they are responsible for the governance of their data. It's often a scary conversation for both parties. But it's necessary and you can show people why it makes sense and adds value to their work too! 20. Relatedly, link people's pain points to current weaknesses in the data governance. Show them they are causing issues for themselves and give them an easier path to fix it without having to learn everything about data work. 21. Data governance doesn't have to be some wholly - or holy 😅 - separate practice. It should just be part of normal work related to data. Make it less scary and more approachable for your business stakeholders. It's a team effort and it drives real, measurable benefits and value. Learn more about Data Mesh Understanding: https://datameshunderstanding.com/about [https://datameshunderstanding.com/about] Data 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/ [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/ [https://datameshlearning.com/community/] If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here [https://docs.google.com/document/d/1WkXLhSH7mnbjfTChD0uuYeIF5Tj0UBLUP4Jvl20Ym10/edit?usp=sharing] All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm [https://pixabay.com/users/lesfm-22579021/], MondayHopes [https://pixabay.com/users/mondayhopes-22948862/?tab=audio], SergeQuadrado [https://pixabay.com/users/sergequadrado-24990007/], ItsWatR [https://pixabay.com/users/itswatr-12344345/], Lexin_Music [https://pixabay.com/users/lexin_music-28841948/], and/or nevesf [https://pixabay.com/users/nevesf-5724572/]