Data Mesh Radio

Key takeaways:

• As mentioned the last two times, at the start, it's more important to start measuring something than it is to measure the right things. Do NOT let analysis paralysis hold you back. Start measuring early to figure out what actually matters and that will also change over time.
• Similarly, your success metric measurement framework will probably suck to start. Oh well, get to measuring.
• Use fitness functions. Episode #95 with Dave Colls covers a lot on this.
• Data mesh really is a journey and your success measurement will be too. You will need to find small and simple ways to measure. Don't get bogged down. Your measurements will be rough and kinda depressing with the amount of challenges to tackle at the start. Just understand this is about how well you are doing, not how complete you are - there is always more to do!
• Reflect back on how far you've come, we often forget to do that!
• When it comes to data quality measurement at the implementation level, you need to think about what are you trying to accomplish. Many people go down the wrong path of trying to measure quality in a vacuum. It's about what are the expectations and why do we care about quality - to improve our decision making around data and to improve trust so more people feel they can rely on data. It's that simple. Now, measuring how well you are achieving those gets a bit harder… :D
• So, what to measure or consider how to measure regarding data quality at the implementation level: how often are people in compliance with their quality SLAs, whatever those SLAs may be? How quickly are you detecting and resolving/recovering from incidents? How many incidents are you having and what is their severity? Who is actually discovering the issues - are there automated detections and is it the producer or consumers discovering them? How do you actually think about trust and the impact of trust on the success of your implementation? How do you measure and increase trust levels? How does that impact value creation? And finally, what is the quality of your metadata?

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,

Use code DATAGOV23 for 35% off ebook copies of Designing Data Governance from the Ground Up here: https://pragprog.com/titles/lmmlops/designing-data-governance-from-the-ground-up/

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 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.

Lauren's LinkedIn: https://www.linkedin.com/in/laurenmaffeo/

Designing Data Governance from the Ground Up (Lauren's book): https://pragprog.com/titles/lmmlops/designing-data-governance-from-the-ground-up/

In this episode, Scott interviewed Lauren Maffeo, author of the book Designing Data Governance from the Ground Up and adjunct Lecturer at George Washington University. To be clear, she was only representing her own views on the episode.

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

1. In governance, a very easy way to go down a bad path is to not automate your standards. Making governance an easy aspect of data work will go a long, long way.
2. ?Controversial?: As an industry in general, data governance maturity is still at the infancy phase. And the pace of maturation is far lower than other aspects of software like security.
3. The majority of organizations are not mature enough with data governance to get a lot of value from things like ML or NLP.
4. Data governance best practices are hard to come by. There isn't really even a large community specific to data governance for people to easily exchange ideas.
5. If the...

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

Due to health-related issues, we are on a temporary hiatus for new episodes. Please enjoy this rerelease of episode #130 with my partner in our weekly data mesh roundtables Jean-Georges Perrin. There are a lot of interesting things to take away from this. A biggie is to have an early thesis about what to drive towards - what will drive value early? Doing data mesh doesn't simply create value. And you need to build momentum. There's a lot here to learn about how to apply good software engineering practices to data with data mesh.

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.

Data Mesh at PayPal blog post: https://medium.com/paypal-tech/the-next-generation-of-data-platforms-is-the-data-mesh-b7df4b825522

JGP's All Things Open talk (free virtual registration): https://2022.allthingsopen.org/sessions/building-a-data-mesh-with-open-source-technologies/

JGP's LinkedIn: https://www.linkedin.com/in/jgperrin/

JGP's Twitter: @jgperrin / https://twitter.com/jgperrin

JGP's YouTube: https://www.youtube.com/c/JeanGeorgesPerrin

JGP's Website: https://jgp.ai/

In this episode, Scott interviewed Jean-Georges Perrin AKA JGP, Intelligence Platform Lead at PayPal. JGP is probably the first guest to lean

Due to health-related issues, we are on a temporary hiatus for new episodes. Please enjoy this rerelease of episode #65 with Abe Gong all about how people are implementing data contracts in the wild. There are so many ways people can just do only defensive data contracts and I think that is such a missed opportunity. Maybe it's where you will have to start but there's a much better way and we talk a bit about why I think that is so distressing that people aren't talking to each other.

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

Abe's Twitter: @AbeGong / https://twitter.com/AbeGong

Abe's LinkedIn: https://www.linkedin.com/in/abe-gong-8a77034/

Great Expectations Community Page: https://greatexpectations.io/community

In this episode, Scott interviewed Abe Gong, the co-creator Great Expectations (an open source data quality / monitoring / observability tool) and co-founder/CEO of Superconductive.

One caveat before jumping in is that Abe is passionate about the topic and has created tooling to help address it. So try to view Abe's discussion of Great Expectations as an approach rather than a commercial for the project/product.

To start the conversation, Abe shared some of his background experience living the pain of unexpected upstream data changes causing data chaos / lots of work to recover from and adapt. Part of where we need to get to using something like data contracts is to remove the need to recover in addition to adapting and move towards controlled/expected adaptation. Abe believes that the best framing for data contracts is to think about them as a set of expectations.

To define expectations here, this would include not just schema but also the content of data, such as value ranges/types/distributions/relationships across tables/etc. So for instance, a column may be a one to five for rankings and then the application team changes it one to 10. The schema may not be broken - it is still passing whole numbers - but the new range is not within expectations so the contract is broken.

At current, Abe sees the best way to not break social expectations is via

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

Scott Hawkins' LinkedIn: https://www.linkedin.com/in/scott-hawkins-8934393/

In this episode, Scott interviewed Scott Hawkins, Principal Data Architect at ITV.

Scott views data mesh as a mechanism for change. Your company culture and your understanding of it are crucial to establishing data mesh well, driving that buy-in. Ask yourself: what challenges does data mesh actually address and hopefully solve, how will it impact the business not the tech, and what does it change. Your organization might not be ready for data mesh. Or a specific domain might not be ready. And that's okay!

As ITV moved forward, they found a "good enough" solution via a global ID. It's not perfect, there might be some overlap - such as one person might have a different global ID for their online subscription versus their broadcast subscription - but it is far better than what they were doing. And it allows for interoperability/joins across the data. This is a big improvement - don't let perfect be the enemy of good or done.

One thing working for ITV is deploying a "team-in-a-box" to help domains move forward - similar to an internal consulting team. Each situation is different so each box they are given is different. The team-in-a-box concept also means it is somewhat easier to build common best practices internally. Coming to the table with defaults has really helped ITV.

Per Scott, there are 3 good ways to drive buy-in for the domain teams:

1. At the senior level - so it trickles down as the management for the team is bought in.
2. Via a strong carrot - solve a problem for them as a kind of quid quo pro / mutually beneficial solution. Trying to solve an unrelated problem will drive lower buy-in.
3. Work on realigning the team KPIs/OKRs with the senior leaders to actually realign incentives.

Continuing on the driving buy-in, Scott recommends working with the domain managers to generate a viable/valuable carrot for the entire team. Explain to those leaders why it matters, work with the leaders to revamp the KPIs if the KPIs are getting in the way of delivering a good data product. This is why exec-level buy-in is so crucial - it is pretty hard to start modifying team KPIs/OKRs...

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

Due to health-related issues, we are on a temporary hiatus for new episodes. Please enjoy this rerelease of episode 150 with Carlos Saona. eDreams' approach is very unique and interesting because it was essentially all on its own so there are a ton of useful learnings to consider if they are the right fit for your own organizations.

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.

Carlos' LinkedIn: https://www.linkedin.com/in/carlos-saona-vazquez/

In this episode, Scott interviewed Carlos Saona, Chief Architect at eDreams ODIGEO.

As a caveat before jumping in, Carlos believes it's too hard to say their experience or learnings will apply to everyone or that he necessarily recommends anything they have done specifically but he has learned a lot of very interesting things to date. Keep that perspective in mind when reading this summary.

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

1. eDreams' implementation is quite unique in that they were working on it without being in contact with other data mesh implementers for most of the last 3 years - until just recently. So they have learnings from non-typical approaches that are working for them.
2. You should not look to create a single data model upfront. That's part of what has caused such an issue for the data warehouse - it's inflexible and doesn't really end up fitting needs. But you should look to iterate towards that standard model as you learn more and more about your use cases.
3. ?Controversial?: Look to push as much of the burden as is reasonable onto the data consumers. That means the stitching between data products, the compute costs of consuming, etc. They get the benefit so they should be taking on the burden. Things like data quality are still on the...

Due to health-related issues, we are on a temporary hiatus for new episodes. Please enjoy this rerelease of episode 133 with Ammara Gafoor. There is a ton to learn from this one and reflect back on.

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.

[email protected]

Ammara's LinkedIn: https://www.linkedin.com/in/ammara-gafoor/

Data Mesh in practice article series from Ammara and colleagues:

#1: https://www.thoughtworks.com/en-us/insights/articles/data-mesh-in-practice-getting-off-to-the-right-start

#2: https://www.thoughtworks.com/en-us/insights/articles/data-mesh-in-practice-organizational-operating-model

#3: https://www.thoughtworks.com/en-us/insights/articles/data-mesh-in-practice-product-thinking-and-development

#4: https://www.thoughtworks.com/en-us/insights/articles/data-mesh-in-practice-technology-and-the-architecture

In this episode, Scott interviewed Ammara Gafoor, Principal Business Analyst at Thoughtworks who has been working on a few client projects related to data...

Due to health-related issues, we are on a temporary hiatus for new episodes. Please enjoy this rerelease of episode 177. As stated in the original show notes, this is one to revist often as it is a great level-setting on why are we doing what we do in data mesh.

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