Data Mesh Radio

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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 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.

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:

1. 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.
2. ?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.
3. 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%.
4. 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.
5. 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 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.

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:

1. 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.
2. 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.
3. "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?"
4. ?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.
5. "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."
6. 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 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.

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

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.

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:

1. "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?
2. Always focus on who you are building a product for, why, and what is the impact.
3. 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.
4. 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?
5. 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.
6. 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.
7. ?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 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.

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:

1. 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.
2. 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.
3. 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.
4. 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 😅
5. 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.
6. Data mesh won't work if you overly decentralize. You must find your balances between centralization and decentralization yourself.
7. ?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...

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.

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...

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:

1. 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?
2. The key to a good platform is something your users _want_ to use not simply must use. That's your user experience measuring stick.
3. 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.
4. 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.
5. ?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:

1. 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 😅
2. 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.
3. Relatedly, per a survey, most organizations understand they aren't capable of training their own LLMs from scratch at this point.
4. 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.
5. 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.
6. Similarly, there is a trend towards organizations exploring more domain-specific models instead of general purpose models like ChatGPT.
7. ?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.
8. 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

Announcing moving to one episode per week :)