Data Mesh Radio

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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.

Kim's LinkedIn: https://www.linkedin.com/in/vtkthies/

Gemba Walk explanation #1: https://kanbantool.com/kanban-guide/gemba-walk

Gemba Walk explanation #2: https://safetyculture.com/topics/gemba-walk/

PayPal Data Contract Template OSS: https://github.com/paypal/data-contract-template/tree/main/docs

Start with why -- how great leaders inspire action | Simon Sinek | TEDxPugetSound: https://www.youtube.com/watch?v=u4ZoJKF_VuA

In this episode, Scott interviewed Kim Thies, at time of recording a Leader on the Enterprise Data Team at PayPal and now SVP, Client Innovation & Data Solutions at ProfitOptics. To be clear, she was only representing her own views on the episode.

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

1. When talking about data mesh to execs, it's helpful to go back to basics: "these are the four main principles, and this is what we've built and why." Scott note: I recommend you slightly alter the phrasing, especially around "Federated Computational Governance" ;)
2. Look to Simon Sinek and "Start with the Why". Always investigate the why for the other party. What would be enticing to your business execs to lean in on data mesh? But data mesh for the sake of data...

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

Key summary points:

• At the start, it's more important to start measuring than it is to measure the right things. Do NOT let analysis paralysis hold you back.
• Similarly, your success metric measurement framework will probably suck to start. Oh well, get to measuring.
• Create a framework and tooling/platform capabilities - where necessary/useful - to make measuring and reporting against success metrics simple. That framework should be about defining the metrics and especially how to measure, not what success looks like for individual data products.
• Use fitness functions
• Good metrics to consider in order of usefulness: user satisfaction, user value, data quality, time to business decision, delivery to expectations, time to update (can be squishy), and usage

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

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.

Smriti's LinkedIn: https://www.linkedin.com/in/smritikirubanandan/

Smriti's HLTH Forward Podcast: https://hlthforward.buzzsprout.com/

In this episode, Scott interviewed Smriti Kirubanandan, a Healthcare and Public Health Data Expert at a large consulting firm. To be clear, she was only representing her own views on the episode. Much of the challenges and opportunities discussed in this episode are more on the US side because of the not-so-well-functioning healthcare system there.

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

1. In healthcare, it's easy to lose sight of the patient in the data - focusing solely on a condition, an area of the body, or a set of data instead of a person. It's vitally important to be focused on the data through a lens of treating the patient as an entire person.
2. !Controversial!: It can sound time consuming to interact with data "in a much more intimate format" much like a 1:1 conversation but it's very important to drive to better outcomes. Instead of automated decisioning, we can point our tooling to compile the relevant information better to make decisions faster without removing the care or the person. Machines making automated decisions leads to worse patient outcomes.
3. "Obviously, privacy is important. Ethics is important. How do we interconnect this data and how do we get to communicate amongst" the payers and providers? So physicians can look at a much more complete picture of the patient to treat them better.
4. There are many organizations collecting important health data about people. We need to rally around the patient outcomes...

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 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.

Frannie's LinkedIn: https://www.linkedin.com/in/frannie-farnaz-h-a7a11014/

Jill's LinkedIn: https://www.linkedin.com/in/jillianmaffeo/

Alla's LinkedIn: https://www.linkedin.com/in/allahale/

In this episode, guest host Frannie Helforoush, Technical Product Manager/Data Product Manager at RBC Global Asset Management (guest of episode #230) facilitated a discussion with Alla Hale, Senior Data Product Manager - Digital Capabilities at Ecolab (guest of episode #122), and Jill Maffeo, Senior Data Product Manager at Vista (guest of episode #151). As per usual, all guests were only reflecting their own views.

The topic for this panel was broadly data product management and the role of the data product manager in a data mesh implementation. Data Product Manager is still a very nascent role so there is still a lot of confusion around it :) If I were to sum up the feeling of the conversation very succinctly, it would be: it's early days, have patience.

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. The role of the data product manager is pretty wide-ranging. It's easy to get overwhelmed and not focus on what really matters. We have to be patient as we learn best practices around data product management because it's still a nascent space.
2. It's crucial to focus on who 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.

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.

Tania's LinkedIn: https://www.linkedin.com/in/sofia-tania/

Presentation: "Data Mesh testing: An opinionated view of what good looks like": https://www.youtube.com/watch?v=stNZQESndAA

In this episode, Scott interviewed Sofia Tania (she goes by Tania), Tech Principal at Thoughtworks. To be clear, she was only representing her own views on the episode. Scott asked her to be on especially because of a presentation she did on applying testing - especially important for data contracts - in data mesh.

Scott note: I was apparently getting extremely sick throughout this call so if I ramble a bit, I apologize. Tania's dog also _really_ wanted to be part of the conversation so you might hear us both chuckling a bit about her antics. And Tania has some really great insights so I probably asked her probably the hardest questions of any guest to date. She did a great job answering them though! A lot of the takeaways are about are we actually ready to do a lot of the necessary testing to ensure quality around data, which I don't think has a clear answer yet :)

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

1. We have to bring software best practices to data but we should do it smartly and not make the same mistakes we made in software, let's start from a leveled up position. Zhamak has said the same. The question becomes how but looking at how practices evolved in software should bring us a lot of learnings.
2. Just pushing ownership of data to the domains won't suddenly solve data quality challenges. The new owners - the domains - have to really understand what ownership means and what quality...

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.

Takeaways:

1. We should be thinking about how we can get out of the batch mode into the streaming mode. Yes, technologically but also think about
2. How can we get to making decisions based on smaller amounts of data more frequently - both automated systems like AI but also for our people. Instead of making adjustments or decisions based on big batches of data, we can make smaller course corrections.
3. "Data mesh is about building responsibility into data and the quality of the data you share and being explicit about that quality."
4. Make the cost of mistakes that much smaller by creating smaller decisions that add up to the bigger decisions - it's not one giant leap, it's many steps that can avoid more hazards as you come across them.
5. "Make decisions at the speed of the market" is crucial to being nimble, being able to react to opportunities or new challenges. To do that, we need to put data in the hands of those closest to the market, the domains.

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. 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.

Brenda's LinkedIn: https://www.linkedin.com/in/brenda-contreras-9649a47/

In this episode, Scott interviewed Brenda Contreras, VP of Engineering and Architecture at Self Financial.

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

1. "Iterate small and sell your solutions on a practical level."
2. It's kind of funny how often people in tech try to skip the communication. If you really align on communication and understanding, your business partners are far more likely to empower you to drive business value for them through engineering and data work.
3. ?Controversial?: As an engineering/data leader, don't dictate: set the vision, explain the vision to business partners, but try to let your technical team leverage patterns that will work for them instead of only your favorite way.
4. Similarly, make sure your team understands which aspects of target outcomes drive value and why. They might have an approach you didn't expect but if they aren't focused on the key aspects of the outcome, even amazing feats of engineering won't create value if it's not tied to business needs.
5. Fail fast is very important to doing microservices right. How can we learn to adopt it in data and AI? "We need we need to be … able to experiment more, we need to be more flexible" to really drive to business value quicker and easier.
6. Before you start to decompose anything, it's crucial to understand what you already have. That can sound a bit obvious but if you start trying to do the work before understanding the 'before' picture, getting to a good 'after' picture is going to be very...