As the name suggests, conceptual data modeling is most relevant at the conceptual stage, when an organization drafts a rough plan with the intention to work out the finer details later. Usually created by data architects and business stakeholders, conceptual data models give stakeholders an easily digestible snapshot of the relevant concepts or entities and the relationships between them. By communicating the model in a way that is relevant to stakeholders who aren’t necessarily tech- and/or detail-oriented, modelers are more likely to get support for their projects. The erwin platform was built with fostering this sort of collaboration in mind.
The aim of a conceptual data model is to provide a data-centric perspective of the organization by documenting how different business entities relate to one another. This is often achieved via entity relationship diagrams (ERD) and/or object-role models (ORM). Unlike logical and physical data models, conceptual data models are technology- and application-independent. This means they are untethered from the reality and context of systems and processes currently in place.
Conceptual data models demonstrate both as-is and to-be states, meaning they are inclusive of changes to the business that are in the works or highly likely. This way, organizations can future-proof the model to a degree, and account for any flexibility that might need to be built into the solution. It’s considered a best practice to distinguish between the as-is and to-be states via color scheme.
Conceptual data models are used in the earlier stages of data modeling to organize and define concepts and rules based on use-case requirements. They are the least detailed of the three types of data models, but by no means does this make them less useful. In fact, one of the key benefits of conceptual data models is that they can be quickly comprehended and communicated to stakeholders outside the “tech” bubble.
Conceptual data models provide organizations with a starting point that should be evolved into more context rich diagrams as they move through the stages of data models. Through use-case analysis, use-case design and database design, the complexity and level of detail will eventually peak with physical data models.