A **logical data model** is a representation of an organization's data that defines its structure, relationships, and constraints independently of any specific [[Database|database]] technology or physical storage mechanism. It occupies the middle tier of the [[ANSI-SPARC Architecture|ANSI-SPARC three-schema architecture]], sitting between the high-level [[Conceptual data model|conceptual data model]] and the technology-specific [[Physical data model|physical data model]]. Logical data models typically express [[Entity (computing)|entities]], [[Attribute (computing)|attributes]], and [[Entity–relationship model|relationships]] using formal notations such as [[Entity–relationship model|entity–relationship diagrams]] or the [[Relational model|relational model]]. They enforce [[Data integrity|data integrity]] through [[Primary key|primary keys]], [[Foreign key|foreign keys]], and [[Database normalization|normalization]] rules, often to [[Third normal form|third normal form]]. Unlike conceptual models, logical models specify [[Data type|data types]] and cardinality; unlike physical models, they omit implementation details such as [[Index (database)|indexes]], [[Partition (database)|partitions]], and storage allocation. Logical data modeling is a central activity in [[Data architecture|data architecture]] and [[Systems analysis|systems analysis]], providing a stable, technology-neutral blueprint that guides [[Database design|database design]] and supports communication between business stakeholders and technical teams.