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A conceptual data model is the most abstract-level data model or summary-level data model. Information specific to the platform and other implementation information such as interface definition or procedures are eliminated from this data model. A conceptual data model is useful due to its simplicity. It is often used for communicating ideas and in strategic data projects.
A conceptual data model is also known as a conceptual schema.
A conceptual data model provides in-depth coverage of business concepts and is mostly developed for a business audience. It is never a solution model and is technology and application neutral in nature. In other words, from a data perspective, the conceptual data model is a business model. Business makes use of the conceptual data model for confirmation and corrections. As they are higher-level models, attributes are usually not added to conceptual data models. They help in establishing relationships between entities, though may not provide the null ability and cardinality properties. Conceptual data models are often designed to be independent of any data storage technologies or database management systems (DBMS). Often conceptual data models are created as part of the initial requirement-gathering efforts, as these models help in exploring high-level concepts as well static business structures. Conventional teams make use of conceptual data models as precursors or as alternatives to logical data models (LDMs).
A conceptual data model helps in identifying high-level key business and system entities and establishing the relationships existing between them. It also helps in defining the key issues of problems which need to be addressed by the system. It can address both digital and non-digital concepts. A conceptual data model can also help in closing the gaps between a solution model and requirements document.