What Is a Conceptual Data Model?
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.