Data Mesh Radio

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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 here

Data 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 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.

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:

1. 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.
2. 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.
3. 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 this
4. Far 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.
5. ?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?
6. 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.
7. ?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 here

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!

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 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 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 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.

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:

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

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 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.

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:

1. 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 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.

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:

1. 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?
2. 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.
3. 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!
4. 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.
5. 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.
6. ?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.
7. 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 shifts
8. In the same vein, make small bets where small bets will do but don't be afraid to make big bets when necessary.
9. ?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 here

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