Data Mesh Radio

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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.

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

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.

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 (info gated) here.

Elena's LinkedIn: https://www.linkedin.com/in/elenasamuylova/

Evidently AI on GitHub: https://github.com/evidentlyai/evidently

Evidently AI Blog: https://evidentlyai.com/blog

In this episode, Scott interviewed Elena Samuylova, Co-Founder and CEO at the ML model monitoring company - and open source project - Evidently AI.

This write-up is quite a bit different from other recent episode write-ups. Scott has added a lot of color on not just what was said but how it could apply to data and analytics work, especially for data mesh.

Some key takeaways/thoughts this time specifically from Scott's point of view:

1. A good rule of software that applies to ML and data, especially mesh data products: "If you build it, it will break." Set yourself up to react to that.
2. Maintenance may not be "sexy" but it's probably the most crucial aspect of ML and data in general. It's very easy to create a data asset and move on. But doing the work to maintain is really treating things like a product.
3. ML models are inherently expected to degrade. When they degrade - for a number of reasons - they must be retrained or replaced. Similarly, on the mesh data product side, we need to think about monitoring for degradation to figure out if they are still valuable or how to increase value.
4. Data drift - changes in the information input into your model, e.g. a new prospect base - can cause a model to not perform well, especially against this new segment of prospects. That data drift detection could actually be a very...

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

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.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here.

Argyris Argyrou's LinkedIn: https://www.linkedin.com/in/argyrisargyrou/

Konstantinos "Kostas" Siaterlis' LinkedIn: https://www.linkedin.com/in/siaterliskonstantinos/

In this episode, Scott interviewed Argyris Argyrou, Head of Data, and Konstantinos "Kostas" Siaterlis, Director of Big Data at Orfium. There is a ton of useful information on anti-patterns, what is going well now, advice, etc. in this one.

From here forward in this write-up, A&K will refer to Argyris and Kostas rather than trying to specifically call out who said which part in most cases.

Some key takeaways/thoughts from A&K's points of view:

1. On a data mesh journey: "It's not a sprint, it's a marathon." Pace yourself. It's okay to go at your own pace, don't worry about what other people are doing with data mesh, do what's right for you.
2. Really focusing on the why and showing people results was a far better driver to buy-in and participation than any amount of selling about data mesh as a practice. Calling it data mesh when trying to explain it to people outside the data team didn't go well either...
3. Orfium's "Data Doctor" approach - a low friction and low pressure office hours for a general staff data engineer - has really helped people help with data challenges and in spreading good data practices but without the "Doctor" becoming a bottleneck.
4. The Data Doctor's role is to answer questions and provide guidance but not do the work for people. Then, take what was discussed and the best practice and document it for others to learn from - providing good leverage for scaling best data practices.
5. In a smaller company like Orfium (~250 people), it's hard to justify a lot of full-time heads to implement data mesh. And trying to treat a data mesh implementation like a side-project also creates issues. There isn't a great answer here on exactly what to do except possibly take things slower than most startups are used to. Your data will still be waiting for you a few months later.
6. If you are having difficulty driving broad buy-in, showing people what data mesh can do in action...

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

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.

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

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

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.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here.

Riya's LinkedIn: https://www.linkedin.com/in/riyasingh1/

In this episode, Scott interviewed Riya Singh, Business Insights Manager at Iterable.

Some key takeaways/thoughts from Riya's point of view:

1. ~4 years ago, Iterable was in essentially "spreadsheet hell" with lots of manual data work and no standard way of storing or sharing data across domains. While domains had good data capabilities, the integration and coordination between domains was very difficult at best.
2. Most exec questions can't be answered by the data from a single domain so cross domain data integration became a key factor in Iterable continuing to grow. How could they make crucial decisions informed by data if there was so much manual work to try to integrate ad hoc? Could they really trust something done manually each time?
3. Fast time to market for simple, base level capabilities of their data platform was much more valuable than trying to nail every feature upfront. Data consumers understood it wasn't perfect data at the start but it led to much faster exploratory data initiatives which led to valuable insights sooner.
4. You might have a much higher ROI buying tools than trying to really get by on low-cost but not feature-rich tools. If you build a very cost-efficient data platform that no one wants to use, is that actually valuable? How much time will you spend managing the tools or is it worth it to outsource that to a vendor?
5. Combining data across sales, marketing, and product meant Iterable could tailor marketing messages and find better prospects, measure marketing return on investment (ROI), and cost optimize their operations and product among many other new insights.
6. As teams that previously weren't directly interacting start to have more conversations, gaps in your data - whether in data created/collected or data shared - will emerge. Filling those gaps will mean you can answer more high-value questions to drive the business forward.
7. At Iterable, when there is a specific use-case identified for cross-domain data integration, the central data team takes over ownership of what would be considered a consumer-aligned data set in data mesh terms. With only 4-5 domains, Iterable doesn't need to...

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

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.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here

Marisa's LinkedIn: https://www.linkedin.com/in/marisafish/

Obeya method: https://obeya-association.com/what-is-an-obeya/

MIT course on "The Science of Intelligence": https://cbmm.mit.edu/education/courses/science-intelligence

John Duncan paper on brains executing series of programs: https://web.mit.edu/9.s915/www/classes/duncan.pdf

In this episode, Scott interviewed Marisa Fish, Director of Information Management at American National Bank. To be clear, Marisa was only representing her own views on the episode.

Some key takeaways/thoughts from Marisa's point of view:

1. Understanding your data value supply chain - the way you derive and deliver value from your data - should be the crux of data and analytics work. The data value supply chain breaks down into sharing the data itself, sharing analytical insights about the data, and managing the data. All three are crucial to creating value from your data.
2. Intentionality is crucial - instead of being reactive, stop and ask what are we trying to accomplish and what value will it drive. Then you will focus much more on high value-impact work.
3. Similarly, think about system engineering work as "mission engineering" - what is your mission in doing your work? Does the work you are prioritizing serve the mission?
4. When sharing information, start from: what is the point, what am I trying to drive with this information exchange? Are you trying to share one person's way of thinking or insights or give others the capability to derive their own insights from the new information? Both are very valid and useful but it's easy to talk past each other if you're not on the same page.
5. So much of the way most organizations work with data is about the known knowns - the data consumer knows what data they want and what questions they want to answer with the data. We need to enable people...

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Episode list and links to all available episode transcripts (most interviews from #32 on) here

Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him at community at datameshlearning.com or 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, and/or nevesf