RAG-Ready Patterns for Data Platforms

Chapter 2. The Semantic Backbone: Designing Data Assets for RAG

The ability to unlock data to support decision making is critical for data teams in every organization. It is also one of the most demanding challenges facing such teams, especially when they are responsible for procuring sources of data, building consumable data artifacts, and meeting their decision makers where they are.

In a typical organization, decision makers tend to be domain experts in their areas of accountability and may even possess basic data retrieval skills. But it is uncommon for them to know their way around an organization’s data platform, which typically incorporates databases, data warehouses, reports, dashboards, and many layers of derived assets. This is one of the places where data professionals, typically data analysts and data scientists, step in to leverage an organization’s data platform and bring curated insights to support the decision makers.

The core argument of this chapter is that enterprise data platforms need a semantic backbone. This is the governed path from source meaning and entity identity to portable definitions, serving interfaces, and answers whose provenance can be explained. We will build that backbone through a six-step process:

1. Identify the entity kernel.

2. Capture source semantics in data contracts.

3. Govern semantic change.

4. Organize assets into layered semantic asset taxonomy.

5. Use portable, declarative definitions.

6. Make metadata available through suitable APIs. ...