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.
A conceptual data model should be employed to define and communicate high-level relationships between concepts/entities. In other words, they help an organization see their data – and the relationships between different types of data – in context.
Ideally, they will be visual representations of data in context that tell the story of how an organization operates in particular circumstances. This can help organizations avoid oversights that could cause significant problems down the line. For example, when building or acquiring a new customer relationship management (CRM) system, the need to distinguish between a prospect and a customer might not be clear. But without that distinction, a “new” prospect could actually be an employee of a company with an existing account.
A database that recognizes the distinction between a prospect and a customer also can be modeled to recognize any potential relationship between a new prospect and existing customer, allowing the record to be consolidated. This way, the sales rep(s) and support rep(s) have the context they need to work effectively.
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.
Bypassing the conceptual data modeling stage makes it far more likely for big-picture entity relationships to be missed – such as the customer/prospect distinction referenced above. Additionally, conceptual data models account for cardinality. Cardinality describes what an entity’s relationships to other entities may be, including one-to-one, one-to-many, or many-to-many.
Without conceptual data modeling, such an oversight becomes more likely, the deeper an organization is into the development cycle. This is because the big picture is often lost when teams are focused on design details and under the pressure of deadlines. By building the conceptual database model first, organizations can avoid such oversights and see that vague terminology, so the potential for relationships between entities is considered and defined ahead of time.
Some of the key benefits of conceptual data modeling include:
erwin has been a trusted name in data modeling for more than 30 years. This experience has given us the opportunity to perfect a data modeling product that meets the needs of all members of a customer’s organization, no matter which stage (conceptual, logical, physical) of data modeling they’re in.
Stakeholders in and outside of the tech bubble can collaborate to ensure models benefit from as much context and perspective as possible. erwin Data Modeler by Quest also boasts an arsenal of powerful automation capabilities that speed up the process, reduce human errors and increase efficiency.