concept of self-describing data models 

Self-describing data models are data structures that include metadata along with the actual data. This metadata describes the data's structure, meaning, and constraints, enabling the system to understand and interpret the data without needing external documentation. Key aspects include:

1. **Embedded Metadata:** Data models contain information about data types, relationships, and constraints directly within the data structure itself.

2. **Schema Information:** The model provides schema details such as field names, data types, and data formats, allowing systems to understand the context and structure of the data.

3. **Flexibility:** Makes data more adaptable and portable between different systems, as the data model carries its own descriptive information.

4. **Self-Documentation:** Reduces the need for separate documentation or schema definitions, as the data model inherently describes itself.

Self-describing data models enhance data interoperability, ease of integration, and automated processing by embedding essential metadata within the data itself.