A conceptual data model is a structured business view of the data required to support business processes, record business events, and track related performance measures.
The main elements of the conceptual model are:
Entity: is a person, organization, object, or concept about which information is stored.
Information about entities is captured as attributes: characteristics or traits that describes an entity.
Relationship: a dependency or an association between two entities.
Relationships are further qualified by identifying their cardinality: the number of instances of one entity related to instances of another entity.
This article describes how a business analyst may approach creating a conceptual data model while defining business use cases.
Here is an example of a conceptual model representing the domain of ski resorts:
This model captures five entities and the relationships between them: SkiResort, Summit, Lift, LIftTicket, and ResortStatistics.
Watch the video below to see how this model was created using information from a free Kaggle data set:
Conceptual data modelling is a useful analysis technique that allows to:
- Name key business entities
- Capture relationships between business entities
- Enforce consistent terminology
- Identify concepts that exist independent of technology implementations
- Support business knowledge
For business analysts and architects, it is an excellent tool to have in your toolbox to create a shared understanding of data and business requirements.