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/

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.

Chris' LinkedIn: https://www.linkedin.com/in/charles-dove-b4715723/

In this episode, Scott interviewed Chris (Charles) Dove, Data Architect at Endava. To be clear, he was only representing his own views.

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

1. Data used by only one use case in one way is not how you make money by leveraging data, it's too expensive. Set yourself up to reuse data and make sure the organization is aware of what data is available.
2. ?Controversial?: Tooling around data, especially metadata, has gotten better. But is it good yet? There are still some major fundamental gaps that seem like basic blocking and tackling around sharing data.
3. Data isn't the point, it's merely a vehicle for exchanging information.
4. Far too often there is an implicit understanding of a taxonomy/shared terms in different business units that is actually incorrect which leads to misunderstandings and mismatched data being treated as the same. But it's not easy to make all aspects of all parts of data explicit and easily understandable, we have to invest and find good ways to do that.
5. !Incrementally Important!: Business people in domains often don't understand their own data because it's embedded in an application. So they only experience their data in a context that is already framed for them by the application. So they don't think about someone else not understanding the data inherently when those others _aren't_ experiencing it through the same application.
6. Getting to a 'good enough' level of documentation is crucial to prevent misuse of data based on misunderstandings. But every organization has to figure out what is good enough and how to get there,...

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.

Juha Korpela (Chief Product Officer at Ellie Technologies) facilitated this panel on data modeling in data mesh with Veronika Durgin (Head of Data at Saks) and Kent Graziano (The Data Warrior, former Chief Technical Evangelist at Snowflake). This panel was hosted by the Data Mesh Learning Community in partnership with Data Mesh Radio.

Veronika's Links:

Veronika's LinkedIn: https://www.linkedin.com/in/vdurgin/

Data Vault North America User Group: https://www.meetup.com/dvnaug/

Kent's Links:

Kent's LinkedIn: https://www.linkedin.com/in/kentgraziano/

Kent's Website: https://kentgraziano.com/

Kent's Twitter: https://twitter.com/KentGraziano

Data Vault Alliance: https://datavaultalliance.com/

Juha's Links:

Juha's LinkedIn: https://www.linkedin.com/in/jkorpela/

Ellie Technologies' website: https://www.ellie.ai/

This write-up is from Scott Hirleman's point of view:

As someone without a ton of depth in the data modeling concepts, here are some of my key takeaways that should be taken with a grain of...

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/

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.

This is likely to be an episode to revisit. Zhamak explains a simple concept - data should not be copied unless it is owned by a data product - but the why is multi-layered and important. It might be one of the most important yet underestimated aspect of data mesh because when done right, it truly ensures trust in data - for consumer but also producer. There's a lot of nuance in how Zhamak is thinking about this but the actual application is quite easy :)

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.

Esther's LinkedIn: https://www.linkedin.com/in/esthertham/

In this episode, Scott interviewed Esther Tham, Experience Designer at Thoughtworks. Scott reached out to talk about data ethics based on a post Esther made on LinkedIn.

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

1. When designing your UX (user experience), companies should aim for as little friction as possible when signing up or transacting. For an ethical company, that means collecting as little information as possible to still maximize value of the service to the user.
2. Companies: if you don't need it, don't collect it! It isn't ethical but also it increases your attack surface for a data leak and potentially lowers consumer trust.
3. We don't have the proof points of many companies doing the right thing and disclosing potential issues of sharing information with them in an understandable way. But that would likely increase consumer trust. Is that trust worth more than the hassle to a company? We need companies willing to try being more ethical to really know but it's a cost with a very uncertain upside so not too likely.
4. People need to learn that their personal data has value - and risk - associated with it. Don't give it over without thinking about how it might be used/misused. But most people are nowhere near that thought process yet. Right now, most people are only worried at most about getting scammed, not should this company have my data and how might they misuse it.
5. Ethics isn't just about collection or even usage, protection is also crucial. If you can't protect sensitive information, you shouldn't be collecting it.
6. How can we encourage the general population to really care...

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.

Pink's LinkedIn: https://www.linkedin.com/in/pink-xu/

In this episode, Scott interviewed Pink Xu, Change Manager of Business Impact of Data Products at Vista.

Before we jump in, there are a few specific examples in this to Vista but I think it is incredibly relevant when looking at measuring the impact of your data work. As Pink says, set the objective/goal for the data product and then measure if it met that objective/goal. It isn't the impact framework's job to specifically measure if the objective of the data product is valuable, only to provide an objective way to measure how well did the data product meet its goal.

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

1. Look to standardize the way you measure impact for data products. Much like data observability/SLA metrics, a centralized team shouldn't be the ones focused on measuring or defining the target impact of a data product, only providing the way to measure it.
2. Again like data observability, an impact measurement framework/methodology means people can trust exactly how impact was measured without having to dig into every measurement decision. It's not like grading your own essay, which is a problem with a not impartial measurement.
3. Impact measurement can only go so far. It shouldn't be the only consideration in valuing a data product but without a fair, impartial framework, measuring the value of work becomes all the more difficult.
4. A data product "enables business impact", it cannot create the impact itself if no one uses it. Think about who gets "credit" for the impact - is it the data product creator or the team that acted on the insights from the data product? Look to reward/credit...