RAG-Ready Patterns for Data Platforms

Chapter 1. The Pillars of RAG Readiness

Large language models (LLMs) have captured the imagination of business and technology leaders alike. They can reason, summarize, and generate with astonishing fluency. They can write emails, draft code, and analyze contracts in ways that feel almost human. Yet ask them to answer a simple enterprise question (e.g., “What is our revenue risk in the Asia-Pacific region if customer churn trends continue?”) and they falter. Why? Because they lack the lifeblood of business intelligence: enterprise context.

An LLM without access to an organization’s trusted data is like a gifted consultant who has never opened the company’s books—brilliant, persuasive, but ultimately ungrounded.

Enterprises have invested for decades in data platforms. We built warehouses, then lakes, and now fabrics. We’ve refined pipelines, governed metadata, and rolled out dashboards to every corner of the business. These systems have served us well in the era of descriptive and diagnostic analytics.

For many years, the data platform’s North Star was clear: deliver accurate, timely aggregates through dashboards and reports. Success looked like refreshed revenue charts, consistent key performance indicator (KPI) definitions, and fast slice-and-dice. In that world, the primary consumers were people, the interface was a visualization, and the questions were largely preshaped.

But LLMs don’t want dashboards. They don’t consume KPIs. They need access to the raw, contextual knowledge ...