Context Is the New Schema

In the early days of business intelligence, organizations struggled with fragmented data, inconsistent reporting, and decisions that moved more slowly than the problems they were meant to solve. The breakthrough came with dimensional modeling. Ralph Kimball gave us a structured way to transform raw operational data into something meaningful and query-friendly. It was not just a technical advance, but a trust advance. Analysts could finally explain where a number came from.

Today, AI faces a similar inflection point. The challenge is no longer about building powerful models, but about making them reliable, interpretable, and enterprise-ready. That is where context engineering enters.