https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) Transcript for this episode (https://docs.google.com/document/d/1ls5QawrOffb3VGIfZCmPYHG0v729Ye7Gu7oKRaOYi8c/edit (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?datameshradio (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?datameshradio (here) In this episode, Scott interviewed Andrew Padilla, who runs a data and software consulting company - Datacequia - and serves as editor of the Data Mesh Learning community newsletter.

This one is a bit more philosophical about sharing information/knowledge so it's one to sit and think over. Things in quotes are direct from Andrew.

Some key takeaways/thoughts that come from Andrew's view of data mesh and the data space in general: To move from sharing the 1s and 0s of data to actually sharing knowledge, we need to harmonize data, metadata, and code - "the digital embodiment of knowledge". That's where Andrew hopes the mesh data products can head. Software development isn't cutting it for sharing knowledge. Will data product development? Do we need to move to knowledge-centered development instead? Remains to be seen. We still don't know how to model well - in data - what is going on in the real world. What are the experiences of the organization? Can we really define an "organizational experience"? Event storming tries but seems to fall short often. We must learn to treat organizations like living entities. Organizational experiences cross multiple domains and the types of experiences will change, will evolve - possibly quite quickly. We again have to get better at modeling those and evolving how we share knowledge about the experiences. Knowledge graphs are the best way we have currently for combining information across domains. We still haven't fully figured out how to leverage our cross domain knowledge though. Historically, we've bent our ways of working to the limitations of the machines. We need to spend more time on bending the machines to better match the way humans store, process, and share knowledge. Data centricity is an interesting concept but might take our current imbalance of data versus operational focus too far towards data. But that might be what is necessary to really get to balance. It remains to be seen. But it's crucial to understand a data-first focus isn't necessarily a knowledge-first or knowledge as a first class citizen approach. It's important to understand that mesh data products are a means to an end in data mesh. Yes, they are crucial to sharing information but they are there to serve a purpose, not that they are the purpose. In data mesh, it can be easy to focus too much on creating data products of immediate utility or that are high value in and of themselves. But it's important to think about how data products together create value - and maybe not immediate value - to really drive forward our understanding of the organization's knowledge and experiences.

Andrew started the conversation with his hope and vision for data products - or the data quantum - in data mesh. Historically, data, metadata, and code are not often grouped together and even less frequently are they in harmony. They belong together as that harmony creates a higher level abstraction to share knowledge, not just the 1s and 0s of data. To get data mesh right, Andrew believes you have to really figure out how to build mesh data products with that harmonization in mind....