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

Key points:

• Every organization starts from a different location when heading to Data Meshtopia. Every journey will look different. Try to understand why they chose to do what when - often by just getting in touch and asking them.
• Because the journey looks different, where you put your focus when will depend. Look to the fundamentals of what are you trying to achieve, how to achieve short and long-term value, and how are you making progress.
• You can choose your own journey - there are different paths to getting to Meshtopia, just because you look like another organization doesn't mean you have to tread the same path.
• Think about the old trope of playing to your strengths while shoring up your weaknesses. This doesn't mean ONLY play to your strengths, that is where I am seeing the most failures… Just because you're bad at governance doesn't mean you can ignore it…
• Find your fellowship or fellowships. Find people to constantly stay in touch with and exchange information. They are also your best leverage points quite often: "Hey, we want to do XYZ approach, Scott here says this is a very standard approach from these 10 organizations and here are the pitfalls so we aren't making this up out of our butts."
• Meshtopia isn't some utopia. It's not as if all our problems are suddenly solved - some of the old lingering ones might be but there are new problems to battle too. It's not as if life is blissful the second you get to a better place.

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

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.

Sid's LinkedIn: https://www.linkedin.com/in/siddharthin/

In this episode, Scott interviewed Sid Shah, Head of Product Data and Analytics at Airtel, a large India telecom operator. To be clear, he was only representing his own views on the episode.

Before we jump in, it's important to note that Airtel are doing data mesh on 'hard mode'. Because of regulatory requirements/restrictions, they are all on-prem. That means extra challenges when it comes to securing compute resources.

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

1. Sometimes you have stop/start in data transformation. It's okay, you can rebuild your momentum. It's okay to try to move when you aren't 100% sure.
2. One important thing about data mesh is that it can validate that you aren't the only organization facing a lot of the common challenges that come with high scale and business complexity/velocity around data. You are not alone. Scott note: there's a reason 500+ companies are doing data mesh :)
3. Similarly, you can leverage the stories of other organizations on a mesh journey to get buy-in internally. Their stories can help you explain to your organization that others are using this approach, that this isn't just a problem in your organization.
4. Many data issues in a large organization can probably be traced back to poor ownership in some form or fashion. Can the teams who should own data - the ones who know it best - even own the data if they wanted to? Do they have the tooling and capabilities?
5. What data related pains are universal to your organization? Does data mesh target any of those? Those universal...

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.

Andrew's LinkedIn: https://www.linkedin.com/in/andrewsharp27/

Nicola's LinkedIn: https://www.linkedin.com/in/nicolaaskham/

Kinda's LinkedIn: https://www.linkedin.com/in/kindamaarry/

Jay's LinkedIn: https://www.linkedin.com/in/jaycomoiii/

In this episode, guest host Andrew Sharp, Principal Consultant - Data Governance & Data Protection at The Oakland Group (guest of episode #172) facilitated a discussion with Kinda El Maarry, PhD, Director of Data Governance at Prima (guest of episode #246), Nicola Askham, AKA The Data Governance Coach, an independent data governance consultant (guest of episode #129), and Jay Como, Strategic Advisor at Curate Insights (guest of episode #92). As per usual, all guests were only reflecting their own views.

The topic for this panel was how do we do data governance well including how do we get started around data governance in data mesh. There's a lot to learn about how to improve your governance but there are no blueprints unfortunately. You have to do the work specific to your organization.

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 single biggest misconception around data mesh is that because we call it doing decentralized data, it means...

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.

Amy's LinkedIn: https://www.linkedin.com/in/amy-tang-edwards/

In this episode, Scott interviewed Amy Edwards, Formerly Director of Analytics and Product at Vista. To be clear, she was only representing her own views on the episode.

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

1. A potential Northstar metric for how data driven you are: how often are people getting, interpreting, and actioning on the data themselves versus waiting for an analyst to tell them? Hard to measure but it's where you want to head, where more and more people are making the effort to interact with data.
2. If you want a data product/use case to succeed, the absolute most important thing is an engaged consumer stakeholder - someone who really, really wants the data for a use case and how they want it. If someone isn't leaning in, consider not building something for them. Scott note: this sounds controversial but it is reinforced in almost every data mesh conversation I have.
3. Similarly, if you are building a data product, you should make sure you are very aligned with your stakeholder. Don't be a request dumping ground, build iteratively together.
4. To understand your progress towards being data driven, you need to actually measure things to track changes over time, your progress. You almost certainly will not find the perfect aspects to measure at first and what you measure will evolve. But it starts with measuring something.
5. There's nothing wrong with starting with a using a success metric that you know isn't going to be something you focus on when a data product matures. As products go through phases, so too...

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

Takeaways:

• This is just scratching the surface of generative AI and data mesh - we will have much deeper discussions in future episodes.
• Zhamak believes generative AI has a ton of positive real world potential, especially in data mesh. Scott is more skeptical. But if things like GenAI are only able to be leveraged by a few large companies trying to collect as much information - especially sensitive information - as possible, there are some big potential societal issues that might come from that. We need to democratize the ability to leverage these types of tools.
• ChatGPT set off a frenzy. It can be easy to want to move incredibly fast towards implementing generative AI. But companies don't have the vast amount of data where they can throw moderate - or worse - quality data and get something useful out. Garbage in, garbage out is a real concern.
• Because they have less data than essentially the sum of the internet like OpenAI used for ChatGPT, companies need to focus on providing quality data into an LLM (large language model) in order for it to actually provide good results. Again, otherwise it is garbage in, garbage out.

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.

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

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,...

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.

Paul's LinkedIn: https://www.linkedin.com/in/paul-cavacas-32a36158/

In this episode, Scott interviewed Paul Cavacas, Senior Manager of Data and Analytics at Ocean Spray.

Quick note before jumping in: Ocean Spray is just at the beginning of their journey - in their pre-implementation phase - and there hasn't been a lot of resistance yet internally. That might make a few people jealous 😅 but there's a lot of interesting things Paul is doing to ensure that they are ready to decentralize what makes sense to decentralize at the right time. There is a lot to be gained from not rushing in. Also, apologies that Scott's audio is a bit weird, he had yet to build his makeshift sound studio in the Netherlands.

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

1. As many have stated, asking the data team - especially one person - to become an expert on many different areas of the business just to complete data work for a project just won't scale. At best it creates incredibly concentrated tribal knowledge. Use this point to drive buy-in for decentralizing data ownership.
2. Having someone who really knows your internal IT application landscape well can really help in choosing which initial teams to start with for a data mesh implementation. That person already has good relationships and a deep understanding of your operational plane so you can pick good problem areas and partners.
3. Similarly, build your early buy-in momentum with people that are more likely to be excited to participate in a data mesh implementation. You don't need to convince the most difficult teams to participate at the start....

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