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Taking a needed break to focus on getting healthy. Be back in August!
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
Mirela's LinkedIn: https://www.linkedin.com/in/mirelanavodaru/
In this episode, Scott interviewed Mirela Navodaru, Enterprise and Solution Architect for Data, Analytics, and AI at Swisscom.
Some key takeaways/thoughts from Mirela's point of view:
Specifically at Swisscom, it's not about doing data mesh. They want to make data a key part of all their major decisions - operational and strategic - and data mesh means they can put the data production and consumption in far more people's hands. Data mesh is a way to achieve their data goals, not the goal.When you are trying to get people bought in to something like data mesh, you always have to consider what is in it for them. Yes, the overall organization benefiting is great but it’s not the best selling point 😅 try to develop your approach to truly benefit everyone.Data literacy is crucial to getting the most value from data mesh. Data mesh is not about throwing away the important knowledge your data people have but it's about unlocking the value of the knowledge your business people have to be shared with the rest of the organization effectively, reliably, and scalably.?Controversial? You really have to talk to a lot of people early in your data mesh journey to discover the broader benefits to the organization. That way you can talk to people's specific challenges to get them bought in. When designing your journey, it is important to get input from a large number of people.When talking data as a product versus data products, the first is the core concept and the second is the deliverables. Scott note: this is a really simple but powerful delineation"No value, no party." If there isn't a value proposition, there shouldn't be any action. You need to stay focused on value because there are so many potential places to focus in a data mesh implementation.You have to balance value at the use case level to the domain versus more global value to the organization. At the end of the day, everything you do should add value to the organization but sometimes use cases are... -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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... -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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.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:
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 😅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.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."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.?Controversial?: Another common mesh anti-pattern is trying to get too specific with definitions or... -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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 😅Focus on delivering value through data instead of delivering data and assuming it has value. – “Not all data is created equal.”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.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.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.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.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... -
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.
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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... -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
Olga's LinkedIn: https://www.linkedin.com/in/olga-maydanchik-23b3508/
Walter Shewhart - Father of Statistical Quality Control: https://en.wikipedia.org/wiki/Walter_A._Shewhart
William Edwards Deming - Father of Quality Improvement/Control: https://en.wikipedia.org/wiki/W._Edwards_Deming
Larry English - Information Quality Pioneer: https://www.cdomagazine.tech/opinion-analysis/article_da6de4b6-7127-11eb-970e-6bb1aee7a52f.html
Tom Redman - 'The Data Doc': https://www.linkedin.com/in/tomredman/
In this episode, Scott interviewed Olga Maydanchik, an Information Management Practitioner, Educator, and Evangelist.
Some key takeaways/thoughts from Olga's point of view:
Learn your data quality history. There are people who have been fighting this good fight for 25+ years. Even for over a century if you look at statistical quality control. Don't needlessly reinvent some of it :)Data literacy is a very important aspect of data quality. If people don't understand the costs of bad quality, they are far less likely to care about quality.Data quality can be a tricky topic - if you let consumers know that the data quality isn't perfect, they can lose trust. But A) in general, that conversation is getting better/easier to have and B) we _have_ to be able to identify quality as a problem in order to fix it.Data quality is NOT a project - it's a continuous process.Even now, people are finding it hard to use the well-established data quality dimensions. It's a framework for considering/measuring/understanding data quality so it’s not very helpful to data... -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
Michael's LinkedIn: https://www.linkedin.com/in/mjtoland/
Marta's LinkedIn: https://www.linkedin.com/in/diazmarta/
Sadie's LinkedIn: https://www.linkedin.com/in/sadie-martin-06404125/
Sean's LinkedIn: https://www.linkedin.com/in/seangustafson/
The Magic of Platforms by Gregor Hohpe: https://platformengineering.org/talks-library/the-magic-of-platforms
Start with why -- how great leaders inspire action | Simon Sinek: https://www.youtube.com/watch?v=u4ZoJKF_VuA
In this episode, guest host Michael Toland Senior Product Manager at Pathfinder Product Labs/Testdouble and host of the upcoming Data Product Management in Action Podcast facilitated a discussion with Sadie Martin, Product Manager at Fivetran (guest of episode #64), Sean Gustafson, Director of Engineering - Data Platform at Delivery Hero (guest of episode #274), and Marta Diaz, Product Manager Data Platform at Adevinta Spain. As per usual, all guests were only reflecting their own views.
The topic for this panel was how to treat your data platform as a product. While many people in the data space are talking about data products, not nearly as many are treating the platform used for creating and managing those data products as a product itself. This is about moving beyond the IT services model for your data work. Platforms have life-cycles and need product management principles too! Also, in data mesh, it is crucial to understand that 'platform' can be plural, it doesn't have to be one monolithic platform, users don't care.
Scott note: As per usual, I...
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
Carol's LinkedIn: https://www.linkedin.com/in/carol-assis/
Eduardo's LinkedIn: https://www.linkedin.com/in/eduardosan/
Continuous Integration book: https://www.amazon.com/Continuous-Integration-Improving-Software-Reducing/dp/0321336380
Measure What Matters book: https://www.amazon.com/Measure-What-Matters-Google-Foundation/dp/0525536221
Inspired by Marty Cagan: https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507
Empowered by Marty Cagan: https://www.amazon.com/EMPOWERED-Ordinary-Extraordinary-Products-Silicon/dp/111969129X
In this episode, Scott interviewed Carol Assis, Data Analyst/Data Product Manager and Eduardo Santos, Professor and Consultant, both at Thoughtworks. To be clear, they were only representing their own views on the episode.
From here forward in this write-up, I will be generally combining both Carol and Eduardo's views into one rather than trying to specifically call out who said which part.
Some key takeaways/thoughts from Eduardo and Carol's point of view:
At the end of the day, the team that produces the data will get the most use out of it 9/10 times. Getting teams used to developing with data in mind isn't just useful for the organization, it is for maximizing their own team's success.Continuous integration is a crucial concept in general for learning how to automate and focus on delivering more, which leads to... -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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 -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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...
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
Darren's LinkedIn: https://www.linkedin.com/in/darrenjwoodagileheadofproduct/
Darren's Big Data LDN Presentation: https://youtu.be/vUjoJrl_MEs?si=WzB0sBStVIAyqDJs
In this episode, Scott interviewed Darren Wood, Head of Data Product Strategy at UK media and broadcast company ITV. To be clear, he was only representing his own views on the episode.
Scott note: I use "coalition of the willing" to refer to those willing to participate early in your data mesh implementation. I wasn't aware of the historical context here, especially when it came to being used in war, e.g. the Iraq war of the early 2000s. I apologize for using a phrase like this.
Some key takeaways/thoughts from Darren's point of view:
Overall, when thinking about moving to product thinking in data, it's as much about behavior change as action. You have to understand how humans react to change and support that. You can't expect change to happen overnight - patience, persistence, and empathy are all crucial aspects. Transformation takes time and teamwork.?Controversial?: In data mesh, it's crucial to think about flexibility and adaptability of your approach. Things will change, your understanding of how you deliver value will change. Your key targets will change. Be prepared or you will miss the main point of product thinking in data.When choosing your initial domains and use cases in data mesh, think about big picture benefits. You aren't looking for exact value measurements for return on investment but you also want to target a tangible impact, e.g. if we do X, we think we can increase Y part of the business revenue Z%.Zhamak defines a data product quite well in her book on data mesh. But data as a product is a much broader definition of bringing product management best practices to data. That's harder to define but quite important to get right.When thinking about product discovery - what do data consumers actually need... -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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... -
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Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
Learn more about Data Mesh Understanding: 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/
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 here
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
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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... -
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Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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 😅Your approach to data mesh, at the start and throughout your journey, MUST be adapted to your organization's organizational model and ways of working. Everyone starts from completely different places.Data mesh won't work if you overly decentralize. You must find your balances between centralization and decentralization yourself.?Controversial?: Historically, teams were charged for data work and resources but with something like data mesh, they can manage their data and data costs far more efficiently. Framework processes, tools, and skills help teams to identify which data is valuable for their own or other domains and requires... -
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Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/
If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here
Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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-b43ddbb0f123
Results of Jen's survey 'The State of Data as a Product in the Real World' (NOT info-gated 😎👍): https://pathfinderproduct.com/wp-content/uploads/2023/12/2023-State-of-DaaP-Real-World-Study.pdf?mtm_campaign=daap-study&mtm_source=pp-blog&mtm_content=pdf-daap-study
In this episode, guest host Jen Tedrow, Jen Tedrow, Director, Product Management at Pathfinder Product, a Test Double Operation (guest of episode #98) facilitated a discussion with Martina Ivaničová, Data Engineering Manager and Tech Ambassador at Kiwi.com (guest of episode #112), and Xavier Gumara Rigol, Data Engineering Manager at Oda (guest of episode #40). As per usual, all guests were only reflecting their own views.
The topic for this panel was data as a product generally and especially how can we actually apply it to data in the real world. This is Scott's #1 most important aspect to get when it comes to doing data - especially data mesh - well. It's the holistic practice of applying product management approaches to data. It ends up shaping all the other data mesh principles and is a much broader topic than data mesh is in his view. But it can...
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Please Rate and Review us on your podcast app of choice!
Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/
If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here
Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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=wG2g67RHYyo
ACA 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 😅 Make a platform where people can customize their experience where possible without going overboard.?Controversial?: Few - if any - current tools in data are "aware" of the data product, they are still focused on their specific tasks instead of the target of creating an actual -
Please Rate and Review us on your podcast app of choice!
Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/
If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here
Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.
Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.
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 😅There are 3 approaches to LLMs: train your own, start from pre-trained and tune them, or use existing pre-trained models. Many organizations should focus on the second.Relatedly, per a survey, most organizations understand they aren't capable of training their own LLMs from scratch at this point.It will likely take any organization around three months at least to train their own LLM from scratch. Parallel training and throwing money at the problem can only take you so far. And you need a LOT of high-quality data to train an LLM from scratch.There's a trend towards more people exploring and leveraging models that aren't so 'large', that have fewer parameters. They can often perform specific tasks better than general large parameter models.Similarly, there is a trend towards organizations exploring more domain-specific models instead of general purpose models like ChatGPT.?Controversial?: Machines have given humanity scalability through predictability and reliability. But GenAI inherently lacks predictability. You have to treat GenAI like working with a person and that means less inherent trust in their responses.Generative AI is definitely not the right approach to all problems. As always, you have to understand your tradeoffs. If you don’t feed your GenAI the right information, it will give you bad answers. It only knows what it - Daha fazla göster
