View source for Generic Data Model

A Generic Data Model, also known as a conceptual data model or a domain data model, is an abstract and high-level representation of the data structure and relationships within a particular domain or subject area. It serves as a blueprint for designing and implementing more detailed and specific data models, such as logical data models and physical data models, for various applications, databases, or systems.

Generic data models are intended to provide a common understanding and vocabulary for describing data entities, attributes, and relationships within a domain, facilitating communication and collaboration among different stakeholders, such as business analysts, data architects, and software developers.

Key components of a generic data model include:

#Entities: Entities are the primary objects or concepts in the domain that need to be represented in the data model. They are typically represented by nouns and can be tangible objects (e.g., Customer, Product) or intangible concepts (e.g., Order, Invoice).
#Attributes: Attributes are the properties or characteristics that describe the entities in the data model. They are typically represented by adjectives or nouns and can be single-valued (e.g., Customer Name, Product Price) or multi-valued (e.g., Customer Phone Numbers, Product Categories).
#Relationships: Relationships describe the associations or connections between entities in the data model. Verbs or phrases can represent them and can be one-to-one (e.g., each Customer has one Billing Address), one-to-many (e.g., each Customer can have multiple Orders), or many-to-many (e.g., each Product can be associated with multiple Suppliers, and each Supplier can provide multiple Products).
#Constraints: Constraints define the rules and restrictions that govern the data model, ensuring data integrity and consistency. They can be related to entity uniqueness (e.g., each Customer must have a unique ID), attribute values (e.g., Product Price must be a positive number), or relationship cardinality (e.g., each Order must be associated with exactly one Customer).

Generic data models offer several benefits:

#Improved communication and collaboration: By providing a common vocabulary and understanding of the data structure and relationships, generic data models facilitate better communication and collaboration among different stakeholders.
#Reusability and standardization: Generic data models can be reused and adapted for multiple applications, databases, or systems within the same domain, promoting standardization and consistency.
#Faster development: By serving as a blueprint for more specific data models, generic data models can accelerate the development process and reduce the risk of errors or inconsistencies.
#Easier maintenance and evolution: Since generic data models are abstract and high-level, they are typically easier to maintain and evolve over time, as they are less impacted by changes in specific technologies or implementation details.

In summary, a generic data model is an abstract representation of the data structure and relationships within a domain or subject area, providing a common understanding and vocabulary for describing data entities, attributes, and relationships. Generic data models can serve as a blueprint for designing and implementing more detailed and specific data models, facilitating communication and collaboration among stakeholders, promoting standardization and consistency, and accelerating development and maintenance processes.





== See Also ==
*[[IT Strategy (Information Technology Strategy)]]
*[[IT Governance]]
*[[Enterprise Architecture]]
*[[Chief Information Officer (CIO)]]
*[[IT Sourcing (Information Technology Sourcing)]]
*[[IT Operations (Information Technology Operations)]]
*[[E-Strategy]]






== References ==
<references />