#254 Easing Into a Data Mesh Journey - Ocean Spray's Pre-Data Mesh Preparations - Interview w/ Paul Cavacas

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.
4. Central ownership isn't necessarily bad until things stop scaling. Having that central ownership means less flexibility and agility to react quickly to market changes or opportunities but also less cognitive load on teams. It's a trade-off.
5. Many of your domains really won't understand data ownership. Find ways to slowly transition them into understanding what ownership entails e.g. starting with documentation and SLOs. What data are they sharing and why does it matter? This isn't going to happen overnight.
6. If you aren't building overly complex data products, look to find people within the domain that are somewhat technically savvy - especially if they want to advance their careers - and start to prepare them for data ownership. Those might be your data product developers or data product managers. Scott note: Brian McMillan talked about a plan to do that in episode #26.
7. ?Controversial?: Even if you aren't looking to move fast with your data mesh implementation, lo...