For decades, data modeling has been fragmented by use cases: applications, analytics, and ML/AI. With the emergence of AI, continuous data flows, and the anticipation of data modeling, these siloed approaches creating compartments are no longer sufficient in today's diverse world of data use cases.

Today's practitioners must master end-to-end the numerous data modeling techniques throughout their lifecycle.

This presentation focuses on 'Mixed Model Arts', which advocates for converging different data modeling methods and innovating with new approaches.