As enterprises race to deploy AI agents, many are discovering that the model is only part of the equation. The harder challenge is the data underneath: fragmented across warehouses, lakehouses, operational databases, logs, and SaaS applications, and often lacking the business context an agent needs to investigate a problem rather than simply answer a question. The distinction matters. Where a traditional BI copilot helps users query existing analytics infrastructure, a data analytics agent can independently …