Within a major automotive organization, business stakeholders relied on central Business Intelligence platforms to track performance. However, non-technical users lacked the specialized data literacy required to perform deep, ad-hoc analysis beyond dashboards, leaving many commercial questions unanswered. This data bottleneck mattered immensely because delayed insights directly slowed down market-dependent strategic decisions. The key complexity lay in bridging the gap between raw data structures and human understanding: the system lacked a semantic layer. To make autonomous analysis possible, the core challenge required embedding complex, highly specific automotive business logic into data models so an automated system could interpret metrics accurately.
