The data team's guide to generative AI analytics

This post details how generative AI analytics works by translating natural language to code, speeding up the analysis loop, opening access to non-technical users, maintaining governance through semantic models and editable outputs, and facilitating collaboration. It also outlines how analytics engineers, data scientists, and business users can leverage these capabilities for pipeline development, exploratory analysis, and self-serve data exploration.