Data Mesh Radio

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

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.Alyona's LinkedIn: 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:?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.!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. 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.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.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.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. ?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...

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.Arne's LinkedIn: https://www.linkedin.com/in/arnelaponin/Chris' LinkedIn: https://www.linkedin.com/in/ctford/Foundations of Data Mesh O'Reilly Course: https://www.oreilly.com/videos/foundations-of-data/0636920971191/Data Mesh Accelerate workshop article: https://martinfowler.com/articles/data-mesh-accelerate-workshop.htmlIn 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: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

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.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

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 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.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 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.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._ShewhartWilliam Edwards Deming - Father of Quality Improvement/Control: https://en.wikipedia.org/wiki/W._Edwards_DemingLarry English - Information Quality Pioneer: https://www.cdomagazine.tech/opinion-analysis/article_da6de4b6-7127-11eb-970e-6bb1aee7a52f.htmlTom 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 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.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-platformsStart with why -- how great leaders inspire action | Simon Sinek: https://www.youtube.com/watch?v=u4ZoJKF_VuAIn 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 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.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/0321336380Measure What Matters book: https://www.amazon.com/Measure-What-Matters-Google-Foundation/dp/0525536221Inspired by Marty Cagan: https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507Empowered by Marty Cagan: https://www.amazon.com/EMPOWERED-Ordinary-Extraordinary-Products-Silicon/dp/111969129XIn 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 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.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 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.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 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.Darren's LinkedIn: https://www.linkedin.com/in/darrenjwoodagileheadofproduct/Darren's Big Data LDN Presentation: https://youtu.be/vUjoJrl_MEs?si=WzB0sBStVIAyqDJsIn 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...

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.Wendy's LinkedIn: https://www.linkedin.com/in/wendy-turner-williams-8b66039/Culstrata website: https://www.culstrata-ai.com/TheAssociation.AI website: https://www.theassociation.ai/In this episode, Scott interviewed Wendy Turner-Williams, Managing Partner at both TheAssociation.AI and Culstrata and the former CDO of Tableau.TheAssociation.AI is "a global nonprofit business organization …focused on bridging the disciplines of AI, data, ethics, privacy, robotics, and security." It is focusing on things like networking and knowledge sharing to drive towards better outcomes including ethical AI.Some key takeaways/thoughts from Wendy's point of view:Right now, we try to break up the aspects of data into discrete disciplines - and then work on each completely separately - far too much. Privacy, security, compliance, performance, etc. Instead, we need to focus on the holistic picture of what we're trying to do and why.Communication is key to effective data work and driving value from data. Hire product managers and focus on the why. Break through the historical perceptions of data as a service organization. Drive to what matters - outcomes over outputs - and focus on delivering value."What's the point of being focused on the data if you don't understand the business that the data is supposed to be used for?"?Controversial?: "There is no transformation without automation." If you want data to play a part in transforming the business, you need to focus on automation. Data related work can't be toil work or most won't even do it."You will never be as successful as you can be as a data organization if you're not able to influence your IT partners, your product teams, your business teams."For far too many companies, data is just an afterthought. It's not the core around how they build out initiatives. When you...

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.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

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.Amritha's LinkedIn: https://www.linkedin.com/in/amritha-arun-babu-a2273729/In this episode, Scott interviewed Amritha Arun Babu Mysore, Manager of Technical Product Management in ML at Amazon. To be clear, she was only representing only own views on the episode.In this episode, we use the phrase 'data product management' to mean 'product management around data' rather than specific to product management for data products. It can apply to data products but also something like an ML model or pipeline which will be called 'data elements' in this write-up.Some key takeaways/thoughts from Amritha's point of view:"As a product manager, it's just part of the job that you have to work backwards from a customer pain point." If you aren't building to a customer pain, if you don't have a customer, is it even a product? Always focus on who you are building a product for, why, and what is the impact. Data product management is different from software product management in a few key ways. In software, you are focused "on solving a particular user problem." In data, you have the same goal but there are often more complications like not owning the source of your data and potentially more related problems to solve across multiple users.In data product management, start from the user journey and the user problem then work back to not only what a solution looks like but also what data you need. What are the sources and then do they exist yet?Product management is about delivering business value. Data product management is no different. Always come back to the business value from addressing the user problem.Even your data cleaning methodology can impact your data. Make sure consumers that care - usually data scientists - are aware of the decisions you've made. Bring them in as early as possible to help you make decisions that work for all.?Controversial?: Try not to over customize your solutions but oftentimes you will still need to really consider the very specific needs of your...

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.Nailya's LinkedIn: https://www.linkedin.com/in/nailya-sabirzyanova-5b724310b/In this episode, Scott interviewed Nailya Sabirzyanova, Digitalization Manager at DHL and a PhD Candidate around data architecture and data driven transformation. To be clear, she was only representing her own views on the episode.Some key takeaways/thoughts from Nailya's point of view:When it came to microservices and digital transformation, we aligned our application and business architectures. Now, we have to align our application, business, and data architectures if we want to really move towards being data-driven.To do data transformation well, you must align it to your application architecture transformation. Otherwise, you have two things transforming simultaneously but not in conjunction.It's crucial to involve business counterparts in your data architectural transformation. They know the business architecture best and the data architecture is there to best serve the business. That is a prerequisite to enable continuous business value-generation from the transformation.Re a transformation, ask two simple questions to your stakeholders: What should this transformation enable? How should we enable it? It will give them a chance to share their pain points and their ideas on how to address them. The business stakeholders know their business problems better than the data people

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.Jen's LinkedIn: https://www.linkedin.com/in/jentedrow/Martina's LinkedIn: https://www.linkedin.com/in/martina-ivanicova/Xavier's LinkedIn: https://www.linkedin.com/in/xgumara/Xavier's blog post on data as a product versus data products: https://towardsdatascience.com/data-as-a-product-vs-data-products-what-are-the-differences-b43ddbb0f123Results of Jen's survey 'The State of Data as a Product in the Real World' (NOT info-gated

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.Tom's LinkedIn: https://www.linkedin.com/in/tomdw/Data Mesh Belgium: https://www.meetup.com/data-mesh-belgium/Video by Tom: 'Platform Building for Data Mesh - Show me how it is done!': https://www.youtube.com/watch?v=wG2g67RHYyoACA Group Data Mesh Landing Page: https://acagroup.be/en/services/data-mesh/In this episode, Scott interviewed Tom De Wolf, Senior Architect and Innovation Lead at ACA Group and Host of the Data Mesh Belgium Meetup.Some key takeaways/thoughts from Tom's point of view:Platform engineering, at its core, is about delivering a great and reliable self-service experience to developers. That's just as true in data as in software. Focus on automation, lowering cognitive load, hiding complexity, etc. If provisioning decision specifics don't matter, why make developers deal with them?The key to a good platform is something your users _want_ to use not simply must use. That's your user experience measuring stick.When building a platform, you want to hide a lot of the things that don't matter. But when you start, especially with a platform in data mesh, there will be many things you aren't sure if they matter. That's okay, automate those decisions that don't matter as you find them but exposing them early is normal/fine.Relatedly, make that hiding easy to see through the curtain if the developer cares. Sometimes it matters to 5% of use cases but also often, engineers really want to understand the details just because they are engineers

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.May's LinkedIn: https://www.linkedin.com/in/may-xu-sydney/In this episode, Scott interviewed May Xu, Head of Technology, APAC Digital Engineering at Thoughtworks. To be clear, she was only representing her own views on the episode.We will use the terms GenAI and LLMs to mean Generative AI and Large-Language Models in this write-up rather than use the entire phrase each time :)Some key takeaways/thoughts from May's point of view:Garbage-in, garbage-out: if you don't have good quality data - across many dimensions - and "solid data architecture", you won't get good results from trying to leverage LLMs on your data. Or really on most of your data initiatives

Announcing moving to one episode per week :)

IRM UK Conference, March 11-14: https://irmuk.co.uk/dgmdm-2024-2-2/ use code DM10 for a 10% off discount!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.Ole's LinkedIn: https://www.linkedin.com/in/ole-olesen-bagneux-2b73449a/Piethein's LinkedIn: https://www.linkedin.com/in/pietheinstrengholt/Samia's LinkedIn: https://www.linkedin.com/in/samia-rahman-b7b65216/Liz's LinkedIn: https://www.linkedin.com/in/lizhendersondata/Ole's book The Enterprise Data Catalog: https://www.oreilly.com/library/view/the-enterprise-data/9781492098706/Piethein's book Data Management at Scale (2nd Edition): https://www.oreilly.com/library/view/data-management-at/9781098138851/Liz's blog: https://lizhendersondata.wordpress.com/In this episode, guest host Ole Olesen-Bagneux, Chief Evangelist at Zeenea (guest of episode #82) facilitated a discussion with Piethein Strengholt, CDO at Microsoft Netherlands (guest of episode #20), Liz Henderson AKA The Data Queen, a board advisor, non-executive director, and mentor in digital and data at Capgemini (guest of episode #106), and Samia Rahman, Director of Enterprise Data Strategy, Architecture, and Governance at SeaGen/Pfizer (guest of episode #67). As per usual, all guests were only reflecting their own views.The topic for this panel was modernizing master data management (MDM) and applying that to...

IRM UK Conference, March 11-14: https://irmuk.co.uk/dgmdm-2024-2-2/ use code DM10 for a 10% off discount!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.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

IRM UK Conference, March 11-14: https://irmuk.co.uk/dgmdm-2024-2-2/ use code DM10 for a 10% off discount!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.Sue's LinkedIn: https://www.linkedin.com/in/suegeuens/In this episode, Scott interviewed Sue Geuens, Director of Data Governance and Product Data at Elsevier. To be clear, she was only representing her own views on the episode.We use the phrase MDM to mean master data management throughout the episode.Some key takeaways/thoughts from Sue's point of view:At the end of the day, if you want to do data governance well, it's about the people. Go talk to them, find out their specific needs and desires and work to tailor your language - and presumably your application of policies when possible - to their situations. People want good data, help them get there!Relatedly, get good at telling stories about data work. Get people to lean in and get them involved. Personalize your communication!While policies and standards are crucial, they are about creating better data for the organization. Try to leverage them as a carrot instead of a stick.?Controversial?: Don't talk about someone owning data. That's scary for most. Find ways to get them excited about owning the data without making it scary by using different phrasing.The key to doing data governance well is getting people to care. We need them to care about the data because others have to use it. And that means the people are the most important focus.Data governance is too focused on 'governance' and that means oversight. The word governance has a bad connotation for a reason - it makes many potential allies uncomfortable. So governance folks have to really work to make it less scary.Don't focus so much on the data aspects of data work when talking with stakeholders. It's about achieving outcomes through data, not data work itself. Focus on what gets your business partners excited and that's (unfortunately) usually not...

Key points:Thus far, most of the generative AI stuff Zhamak has seen is not that much of a differentiator. They are doing far better chat bots but that hasn't really changed the game.When it comes to any ML work - and GenAI is just a subset of ML work - engineers need data products to make their data work easy. Reliable sources of data, ability to version, etc. Data mesh obviously plays well there.Relatedly, we need to continue to make things easier for people to leverage data products for GenAI. Engineers shouldn't have to spend all their time moving data around and using many systems.GenAI really could be game changing in data mesh but right now we don't have enough information to really do it well. We need far more metadata around things like data products.GenAI often gives extremely shallow answers that just aren't that helpful. If we can get better answers, amazing. But right now, it's not there.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.Frederik's LinkedIn: https://www.linkedin.com/in/frederikgnielsen/In this episode, Scott interviewed Frederik Nielsen, Engineering Manager at Pandora (the jewelry one, not the music one

Quick Summary PointsTalk to the business strategy importance - data is there to make things better for the business. What could being better informed mean for your execs?When people ask about the strategy, that is when you can mention data mesh. It isn't about doing data mesh but you also aren't inventing this whole-cloth. 100s to 1000s of organizations are already on the journey. But data mesh is not some magic phrase, it is merely a framing for doing data better at scale.Think of the first hidden data demon from my upcoming mini-book: this is about getting to data driven, not being data dragged. This is about better equipping the people you thought were good enough to hire for their expertise and making them even better.Think of the second hidden data demon: data isn't only about strategic decisions - this gets us into a place where we can make better day-to-day execution decisions too.We don't get to skip leg day. I originally typed 'leg data' and maybe that's what we call the foundations

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.Mandeep's LinkedIn: https://www.linkedin.com/in/kaurmandeep80/In this episode, Scott interviewed Mandeep Kaur, Enterprise Information Architect at Nordea Asset Management. To be clear, she was only representing her own views on the episode.Nordea has been on their data mesh journey for a while and Mandeep has been trying to figure out best practices for the hundreds - thousands - of micro decisions in a journey. So how do we get comfortable with making so many calls?Some key takeaways/thoughts from Mandeep's point of view:"1) don't overthink it; 2) bring value out as soon as possible; [and] 3) evolution before completion."The micro decisions in data mesh do matter, give them some thought. But it's important to simply get some perspective from the people who should know best and move forward. That can be from people inside or outside your organization but think about the blast radius of getting something wrong before you fix it. Most times it's smaller than you'd expect.Your first question when considering data mesh: what value am I trying to get out of it? Think about what are the target value propositions and what does it do for the business if this is successful. If you don't have good answers, should you do data mesh?The answers to the 'what value' question of your own mesh journey above should drive your strategy, where you should focus early and what will measure your success. And every organization will have different answers.?Controversial?: There's a LOT of overthinking in most data mesh implementations

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

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.JGP's LinkedIn: https://www.linkedin.com/in/jgperrin/Amy's LinkedIn: https://www.linkedin.com/in/amy-raygada/Andrew's LinkedIn: https://www.linkedin.com/in/andrewrhysjones/Andrew's website: https://andrew-jones.com/daily/Andrew's book: https://data-contracts.com/Data contract standard project Bitol: https://lfaidata.foundation/projects/bitol/JGP's blog: https://jgp.ai/In this episode, guest host Jean-Georges Perrin, Data Innovation Consultant at ProfitOptics (guest of episode #130 and panelist in episode #227), facilitated a discussion with Amy Raygada, Senior Data Product Manager at Swiss Marketplace Group (guest of episode #165), and Andrew Jones, Principal Engineer and Author of the book on Data Contracts (guest of episode #29). As per usual, all guests were only reflecting their own views.The topic for this panel was all about data contracts and how do we go about getting them in place. Much of it was about the general concept but some of it was specifically about how do we think about data contracts applying to data mesh. This was the first topic I really did a deep dive into in early 2022 and it has evolved but is definitely still evolving.Scott note: As per usual, I share my takeaways rather than trying to reflect the nuance of the panelists' views individually.Scott's Top Takeaways:Data contracts are about trust and understanding. Trust that there is an owner and there are rules, there is a minder that knows this data matters. Trust that things...

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.Alexandra's LinkedIn: https://www.linkedin.com/in/dralexdiem/In this episode, Scott interviewed Alexandra Diem, PhD, Head of Cloud Analytics and MLOps at Norwegian insurance company Gjensidige.Gjensidige's approach closely aligns with data mesh but they are starting with a focus on consumer-aligned data products as they have a well-functioning data warehouse and are not looking to replace what isn't broken.Some key takeaways/thoughts from Alexandra's point of view:Advice to past data mesh self: stop talking to people about data mesh, talk to the changes in the way of working. It can be very tiresome to try to explain data mesh instead of those changes. Data mesh isn't the point.There aren't really any reasons we can't apply many software engineering best practices to data, it's simply we haven't done it broadly in the data world.There is a push and pull between software best practices and data understanding. Consider which you see as more important and when. Do you bring data understanding to software engineers or software best practices to those with data understanding.When you leverage pair programming between enablement software engineers and data analysts that understand the domain, the software engineers learn more about data and the domain and the analysts learn good software engineering/product practices. It's a win-win.The people you enable to do work in a data mesh way should serve as ambassadors of your ways of working, especially within the domain. Both helping others learn and as champions. That provides organizational scale. You can't individually enable every person in a large company."Too many cooks spoil the broth." Think about having that 'two pizza team' kind of approach so you have concentrated understanding by those involved in creating data products who then can again help others learn. This is good for those in the domain and also for an enablement team bringing learnings back to a platform team.Having a team with intimate knowledge of what data products/data product features have...

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:We need API-first technologies in data. Not just offering APIs but being able to integrate seamlessly with each other via API. We have that in software but it's been a long-time coming in data. If we want an actual modern data stack, we need to have tooling providers make a real change.Simple made easy: we need to make things simple for data product development and consumption. It's not simplistic but it removes unnecessary complexities.Overall, there is such a trend in data where people aren't building things that remove toil - there is this assumption of increasing complexity of use cases but so much of the work is not that complicated. We need to make it so most people can do most of the work relatively easily without making it overly simplistic - easier said than done of course.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.Ryan's LinkedIn: https://www.linkedin.com/in/ryancollingwood/In this episode, Scott interviewed Ryan Collingwood, Head of Data and Analytics at OrotonGroup. To be clear, he was only representing his own views on the episode.Some key takeaways/thoughts from Ryan's point of view:Have empathy for yourselves and others in all things you do around data. You won't always get it right the first time. Build the relationships, build the trust to continually drive and iterate towards better.In tech, far too often we hear what people need and provide a poor solution to actually solving their needs. It's focusing on the tech instead of the people.Far too many technical solutions/approaches - e.g. data mesh, data contracts, etc. - are really presented for tech-heavy/forward companies e.g. startups. Most companies, large or small, are not capable to leverage the approaches as presented so they must be adapted for 'the rest of us' companies. Scott note: data mesh is like thisFar too often, these tech approaches focus purely on the tech instead of the people. That's partially because every org has a different culture so you can't cover them all; but if you only follow the approach as presented instead of focus on the people/ways of working in your org, it's far less likely to go well. You've implemented a great technical solution that no wants to or can use.?Controversial?: "What are the trade-offs that I can make, while still being true to the value and the benefits that I want to get out of this?" Scott note: SO important to consider when looking at any technical pattern/approach. What is true to the value of the approach?Data contracts really rely on 3 things: at least two parties, an agreement of some kind that is recorded, and access to data that conforms to that agreement. You can add value building beyond those 3 but you have to start somewhere and you can deliver value with something that only satisfies those 3.?Controversial?: It's hard not to have a sense of imposter syndrome when you actually strip a concept...

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

Please Rate and Review us on your podcast app of choice!Sign up for Data Mesh Understanding's free roundtable and introduction programs here: 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 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.Kate's LinkedIn: https://www.linkedin.com/in/katecarruthers/Kate's 'Data Revolution' Podcast: https://datarevolution.tech/In this episode, Scott interviewed Kate Carruthers, Head Of Business Intelligence at the UNSW AI Institute and Chief Data & Insights Officer at UNSW (University of New South Wales). To be clear, she was only representing her own views on the episode.UNSW is not currently implementing data mesh but are preparing to be able to do so. This is a great lesson in building up the capabilities to move forward towards your goals but not rush.Some key takeaways/thoughts from Kate's point of view:Universities can teach us some really interesting perspectives on self-serve. Because universities are such complex organizations and so many departments are involved in deep investigations in very specific areas, they really are the only domain experts. So enabling them to even just own their own data can be very challenging, let alone helping them share with others safely.Relatedly, each academic researcher is essentially a micro-domain themselves with their own ways of working. That just adds to the need to enable freedom in ways of working but still "keep them safe." Scott note: safety was a key theme of the conversation"At the end of the day, data mesh is about controlling the bits that you need to control, and giving people the freedom to do what they need to do, safely.""Technology is kind of the least of your problems." When it comes to data, be prepared to start with some people not even recognizing there is a problem with the current ways of working or a need to improve. Connect their pain to data immaturity to win them over.The best way to win people over is show, don't tell. Show them the power of self-service instead of pitch them on it. Get a PoC going and get people to tangibly see - and hopefully soon touch - your self-service capabilities early.Always look to anchor your data work - especially...

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

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.Benny's LinkedIn: https://www.linkedin.com/in/bennybenford/Iulia's LinkedIn: https://www.linkedin.com/in/iuliavarvara/Nailya's LinkedIn: https://www.linkedin.com/in/nailya-sabirzyanova-5b724310b/Stefan's LinkedIn: https://www.linkedin.com/in/stefan-zima-650229b7/In this episode, guest host Benny Benford, Founder and CEO at Datent - a data transformation focused consultancy/community - and guest of episode #244 facilitated a discussion with Iulia Varvara, Advisory Consultant in Digital and Organizational Transformation at Thoughtworks (guest of episode #268), Nailya Sabirzyanova, Digitalization Manager at DHL (guest of a soon-to-be-released episode), and Stefan Zima, Data Transformation Lead at Raiffeisen Bank International AG (guest of episode #270). As per usual, all guests were only reflecting their own views.The topic for this panel was transformation when it comes to data and data mesh in general but especially understanding how organizational transformation must play a large part in a data mesh implementation to be successful. And that transformation is not simply making changes, it is making _lasting_ changes. Organizational transformation is a crucial aspect of doing data mesh even if it's not spoken about all that often.Scott note: I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views individually.Scott's Top Takeaways:Transformation means changing something. We aren't starting from scratch. You have to consider the starting points, not only the target end points - and in data mesh, there isn't really an end. Every organization's transformation starting point, whether a data mesh transformation or otherwise, will be unique so adjust your...

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.Sean's LinkedIn: https://www.linkedin.com/in/seangustafson/In this episode, Scott interviewed Sean Gustafson, Director of the Data Platform at Delivery Hero.Delivery Hero has been on the data mesh journey for longer than most organizations, at least over 3 years.Some key takeaways/thoughts from Sean's point of view:It's extremely hard but still important to try to impact your culture through things like your data platform. Who are you trying to make information available to? How do you make it accessible? How do you make data ownership easier?A key role of the data platform is that golden/easy path. Showing people easy ways to accomplish what they need with data products. Embed best practices into the platform when possible.You need a product manager in your data platform team. It's easy-ish to build cool things in data but understanding and building to user needs is harder and a must. Treat your data platform as a product!Relatedly, there isn't anything all that special about product management around the data platform. You can take what we've learned from other disciplines - especially software - and tweak it a bit for data. But it's not some arcane art.Focus on KPIs around what you are building and why, especially for your data platform. It's very hard to measure developer productivity but that doesn't mean you just don't measure it.?Controversial?: Be prepared to deal with a lot of qualitative data when measuring success around your data platform. Surveys work far better than most might think.Good product managers balance the short and long-term. You don't want to make drastic and breaking changes to your data platform often but that doesn't mean you can't take bigger bets and shake things up. Just balance iterative improvements and the bigger picture. Scott note: Zhamak talks about Thomas Kuhn and cumulative progress versus paradigm shiftsIn the same vein, make small bets where small bets will do but don't be afraid to make big bets when necessary.?Controversial?: It...

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

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...

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

Important points:There are places where nuance adds value. Many times, explicit definitions around data aspects like quality or even SRE metrics like uptime and query performance are not one.Provide a simple way for producers to apply these scalable approaches - the platform should measure data quality metrics for example.Data producers are having a hard enough time in general learning how to leverage data better. Find places to make it about learning about the information encapsulated in the data product, not learning a new set of SLAs for each data product.Consumers will thank you too since it make their lives easier. With that, you should see more of an uptick in data usage.Please Rate and Review us on your podcast app of choice!Sign up for Data Mesh Understanding's free roundtable and introduction programs here: 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 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.Stefan's LinkedIn: https://www.linkedin.com/in/stefan-zima-650229b7/In this episode, Scott interviewed Stefan Zima, Data Transformation Lead at RBI (Raiffeisen Bank International AG). To be clear, he was only representing his own views on the episode.Some key takeaways/thoughts from Stefan's point of view:No one has data mesh all figured out. Go talk to each other. But also don't be ashamed that you are running into challenges. So is everyone else. Data mesh implementers also need to share more of the anti-patterns they are finding.Agile transformation really focuses a lot on communication and transparency. Both are very crucial to really any successful transformation initiative. Humans struggle with uncertainty and change so giving them a lot of information especially about the why prevents unnecessary pushback. Relatedly, there are many things we can take from Agile transformation practices to apply to data/data mesh transformation. It's not a copy/paste but there's still much that is very relevant with some tweaks.Many organizations are still focusing on technology-led transformation, whether data or digital in general. You must also change the mindset and organizational approaches if you want to be successful.In banking, the rise of fintechs (financial technology companies) has made it clear that being nimble and quickly acting on data is crucial. Being data driven is required to remain competitive.Data mesh can mean far less friction in getting to serving use cases. Instead of fighting against the data protection office, they are involved from the start. That time to market is especially crucial in banking now.If you can, look to make your data sharing policies and approaches generic enough to only create friction when there truly is something different that should be examined further.If you really want to be 'data-driven', if you really want to be a data company, you have to find and address the friction points in your data processes. Stop trying to simply get better at processes that...

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

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.Vanessa's LinkedIn: https://www.linkedin.com/in/vanessaeriksson/Sid's LinkedIn: https://www.linkedin.com/in/siddharthin/Stefan's LinkedIn: https://www.linkedin.com/in/stefan-zima-650229b7/Duncan's LinkedIn: https://www.linkedin.com/in/duncan-cooper-1113722/In this episode, guest host Vanessa Eriksson, the first CDO in Sweden and the head of data advisory company Vanessa Eriksson AB facilitated a discussion with Duncan Cooper, Chief Data Officer for Northern Trust Asset Servicing, Sid Shah, Head of Data Monetization and Platform at Airtel (guest of episode #258), and Stefan Zima, Data Transformation Lead at Raiffeisen Bank International AG (guest of episode #270). As per usual, all guests were only reflecting their own views.The topic for this panel was about the leader's role in a data mesh implementation and what these four panelists have learned in that role. This was the second iteration of a panel we will likely have about every six months or so - the first was episode #215 from April of 2023.Scott note: I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views individually.Scott's Top Takeaways:Regarding data mesh: get going but don't rush. Essentially, get started now but don't be in a hurry to try to get to some picture perfect end state. You need to take your time to make sure you are transforming instead of making changes that will unravel. Be brave and move forward into some uncertainty!Relatedly, you will absolutely get many things "wrong" but wrong in a data mesh world can simply mean not right yet. We have ways to adapt/adjust and evolve as we learn and grow. Data mesh provides you the ability to iterate towards better constantly.You _really_ should...

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 if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Iulia's LinkedIn: https://www.linkedin.com/in/iuliavarvara/In this episode, Scott interviewed Iulia Varvara, Advisory Consultant in Digital and Organizational Transformation at Thoughtworks. To be clear, she was only representing her own views on the episode.Some key takeaways/thoughts from Iulia's point of view:If you are greatly changing your general approach to something - which data mesh does in many ways - you need to focus some amount on actual transformation. These approaches are not a switch you flip, it takes time and concerted effort to make lasting changes that work well.If an organization hasn't really broadly embraced product thinking, starting with data as a product/product thinking in data can act as a catalyst for other aspects of the business to embrace product thinking.You don't change the organizational mindset through words - you start using new ways of working that change people's mindset as they see the benefit of those ways of working. At the end of the day, talk is cheap.To do data mesh well and have it work for an organization, it's best to tailor to their existing ways of working. Yes, change is necessary but a revolution is far less likely to work than an evolution. How are teams working and where can we make smaller tweaks?Because you need to tailor your implementation to your own organization, any data mesh blueprint that will supposedly work for all organizations is likely to be snake oil at best.?Controversial?: The first two principles of data mesh - domain data ownership and data as a product - have the most impact on the organizational...

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 current data product developer experience sucks. In software, we have one simple interface that manages all the pieces needed to get the job done, packages those components. In data, we have to jump through hoops and interfaces across many tools.Right now, the developer has to manage everything themselves which is a ton of work. But it's also a big risk because of lifecycle management - if everything isn't packaged and deployed as one, you have dependencies drifting from each other.There are many places from software we can learn from as to how to do this containerization. Ruby-on-Rails and CloudFoundry are good places to look. 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