A **conceptual data model** is a high-level representation of an organization's data landscape, defining the major entities of interest, their key attributes, and the relationships between them in terms that reflect business concepts rather than technical implementation. It is the least detailed and most abstract tier of the [[Data model|data modeling]] hierarchy, sitting above the [[Logical data model|logical data model]] and [[Physical data model|physical data model]], and is intended to be comprehensible to both business stakeholders and technical practitioners without requiring knowledge of any specific [[Database management system|database management system]] or technology platform. A conceptual data model typically identifies the primary [[Entity (data modeling)|entities]] within a domain — such as Customer, Product, or Order — and describes how these entities relate to one another at a coarse level of granularity. It does not specify [[Data type|data types]], [[Primary key|primary keys]], [[Foreign key|foreign keys]], or [[Normalization (database)|normalization]] structures; these concerns are deferred to the logical layer. The model is commonly represented using [[Entity–relationship diagram|entity–relationship diagrams]] or similar notations, and may be supplemented by a [[Business glossary|business glossary]] that establishes agreed definitions for each entity and relationship. Conceptual data models serve several purposes within [[Data governance|data governance]] and [[Data architecture|data architecture]] practice. They provide a shared vocabulary that aligns business and technical teams around a common understanding of the data domain, and serve as the authoritative starting point from which more detailed logical and physical designs are derived. They are particularly valuable in [[Master data management|master data management]] and [[Enterprise architecture|enterprise architecture]] initiatives, where establishing consensus on core entities and their boundaries is a prerequisite for integration, [[Data quality|data quality]] improvement, and [[Interoperability|interoperability]] across heterogeneous systems.