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.
So, continuing the conversation about AI and ML’s place in data mesh, we start the episode with Zhamak discussing an unnecessary complication we’ve created in data – why do data sets/assets only have to serve one user or even user persona? Yes, product thinking is about creating reuse but are we thinking reuse across regular analytics and ML/AI at the same time? We need to make it easy to give access in the language of, that native mode of access of, the data consumer. We shouldn’t have to care what it is used for, regular analytics, ML, or anything in between.
There’s also this very painful bifurcation between upstream data production and data science where the second data enters the data science realm of influence, it’s copied over and you lose sight of it for discoverability, governance, security, quality, etc. They pull it in and then it’s essentially impossible to track. That creates all kinds of problems. So why don’t we extend data mesh into what they are doing? Do they need to make copies of the data in the feature store? If they have a trusted source of access to the data, do they care?
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/