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#236 Driving Buy-in For Decomposing the Monolith; and Then Actually Doing It - Interview w/ Brenda Contreras
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:
- "Iterate small and sell your solutions on a practical level."
- 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.
- ?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.
- 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.
- 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.
- 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...
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By Data as a Product Podcast Network
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99 ratings
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:
- "Iterate small and sell your solutions on a practical level."
- 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.
- ?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.
- 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.
- 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.
- 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...