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

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.

So, continuing the conversation about AI and ML's place in data mesh, we start the episode with Zhamak discussing an unnecessary complication we've created in data - why do data sets/assets only have to serve one user or even user persona? Yes, product thinking is about creating reuse but are we thinking reuse across regular analytics and ML/AI at the same time? We need to make it easy to give access in the language of, that native mode of access of, the data consumer. We shouldn't have to care what it is used for, regular analytics, ML, or anything in between.

There's also this very painful bifurcation between upstream data production and data science where the second data enters the data science realm of influence, it's copied over and you lose sight of it for discoverability, governance, security, quality, etc. They pull it in and then it's essentially impossible to track. That creates all kinds of problems. So why don't we extend data mesh into what they are doing? Do they need to make copies of the data in the feature store? If they have a trusted source of access to the data, do they care?

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. You can download their Data Mesh for Dummies e-book (info gated) here.

Ebru's Twitter: @ebrucucen / https://twitter.com/ebrucucen

Ebru's LinkedIn: https://www.linkedin.com/in/ebrucucen/

In this episode, Scott interviewed Ebru Cucen, Lead Consultant at Open Credo. To be clear, Ebru was only representing her own views on the episode.

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

1. It's far too hard for data producers to actually reliably produce clean, trustworthy, and well-documented data. We need to give them a better ability to do that, whether that is tooling or ways of working remains to be seen. Scott note: It's no wonder it's been hard for many teams to get their domains to own their own data ;)
2. There is a hidden challenge in data-intensive service/application development. The version of the data - the schema, the API, and the data itself version - need to be understood and coordinated as the developers don't control their own data sources unlike software development of the past. But we don't have good ways of doing that right now on the process or tooling front - data product approaches help but fall short.
3. We are lacking the tooling to easily manage data quality for producers. While there are so many data related tools, there is a real lack of things that make it easy to manage the quality. We are getting there on observing or monitoring quality, but not managing and maintaining quality.
4. Fitness functions can help you measure if you are doing well on your data quality/reliability.
5. As the speed to reliably ship changes on the application side increased - microservices and DevOps -, that just made the data warehouse, the data monolith that

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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. You can download their Data Mesh for Dummies e-book (info gated) here.

João's LinkedIn: https://www.linkedin.com/in/joaoantoniosousa/

João's Medium: https://joao-antonio-sousa.medium.com/

Brent Dykes' LinkedIn: https://www.linkedin.com/in/brentdykes/

In this episode, Scott interviewed João Sousa, Director of Growth at Kausa.ai. To be clear, he was only representing his own views on the episode.

The "four types" will often be throughout this summary. The four types refers to the types of analytics: descriptive - what is happening; diagnostic - why is it happening; predictive - what might happen in the future; and prescriptive - what actions should we take.

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

1. Of the four types of analytics, diagnostic analytics is VERY underserved. The other three - descriptive, predictive, and prescriptive - are where most organizations are focusing more so there's a "diagnostic analytics gap."
2. ?Controversial?: Of the four, diagnostic analytics requires the most domain/business expertise.
3. Tips for improving your diagnostic analytics: 1) show the value of drilling down in to the why - find a few use cases and communicate the value well; 2) promote a closer collaboration between data and business people; 3) improve your definitions around data roles; 4) very clear communication of expectations and who does what; 5) don't get into firefighting mode, have a structured approach to diagnostic analytics; and 6) automate the repetitive parts.
4. There are 3 levels of diagnostic analytics immaturity: getting "stuck

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.

Humans by our very nature categorize things - otherwise how can we really differentiate? How can we learn about new ideas and experiences if not finding a way to store them in our mental models. And in data, we've been treating diagnostic and descriptive analytics as an entirely different category to the predictive analytics of AI and ML. The way we partition the world in data is around how data will be used and then prepare the data as such, to be very fit for purpose. What if instead we partition around the data domain and don't really care about who or how things are used - we want to serve all consumers - what changes? Can we create data that is simply usable by many? Does that actually reduce complexity overall by not owning data production designed to specific purposes? Do we really need to treat AI/ML as if their consumption is all that different or special?

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 e-book (info gated) here.

Karen Passmore (CEO at Predictive UX) led this discussion with Wannes Rosiers (Product Manager at Raito) and Alice Parker (Data Engineer at DNB). This panel was held in partnership with Data Mesh Learning - you can see a link to the video here: Panel: Data User Experience - An Introduction (Data Mesh Learning and Data Mesh Radio)

Alice's LinkedIn: https://www.linkedin.com/in/aliceparker/

Wannes' LinkedIn: https://www.linkedin.com/in/wannes-rosiers/

Blog post 'The Importance of UI/UX - and why Raito’s first hire was a designer': https://www.raito.io/post/the-importance-of-ui-ux-and-why-raitos-first-hire-was-a-designer

Raito blog: https://www.raito.io/blog

Karen's LinkedIn: https://www.linkedin.com/in/karenpassmore/

Predictive UX: https://www.predictiveux.com/

Some key takeaways from panelist Wannes Rosiers:

1. DUX handles user experience using data for all your users (data producers, data engineers, data analysts, data scientists, report users, ...), hence it is not - and certainly not restricted to - data platform user experience.
2. Data products are typically chained from producer oriented data products up until consumer oriented data products. Downstream...

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. You can download their Data Mesh for Dummies e-book (info gated) here.

Vikas' LinkedIn: https://www.linkedin.com/in/vksnov9/

Vikas' Twitter: @vikaskumar9 / https://twitter.com/vikaskumar9

Vikas' email: vikaskumar9 [at] gmail

In this episode, Scott interviewed Vikas Kumar, AVP and Head of Data, AI, and ML at CNA Insurance. To be clear, he was only representing his own views in this episode.

Some key takeaways/thoughts from Vikas' point of view:

1. In data mesh, make sure to keep focused on bringing the business domains along. You aren't building for the sake of building. If users can't derive value from the data work being done, why is it being done?
2. The 2010s through the early 2020s have been about moving data to the cloud but we are starting to see people really leverage that data to generate value. The cloud unlocks many new possibilities around data due to flexibility, scalability, and unit economics.
3. With moving to cloud, there is much less focus on specifically managing the data and more focus on getting value from the data. SaaS data product offerings really unlock people's time to focus on driving value.
4. Cloud gives us the scale and data availability but there is still a long way between having the data available and leveraging the data for significant value.
5. Cloud can be a double edged sword - it gives you flexibility and scalability but without good controls, you are likely to do a lot of duplicate work. Be careful that ease of data product creation - or at least PoC creation - doesn't create chaos and data product overlap. Make sure to have good governance here including strong communication.
6. ?Controversial?: We aren't very good...