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Conceptual Data Modeling

Get the big-picture perspective of your organization’s database requirements

There are three different types of data models – conceptual, logical and physical. Each data model has a specific purpose, which is primarily defined by the level of operational detail. Conceptual data models are built at the first stage of the data modeling process. They provide a summery-level perspective, omitting finer details in favor of a more readily digestible format.

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.

What Is the Goal of a Conceptual Data Model?

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 tells 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.

When Should I Consider a Conceptual Data Model?

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.

Why Should I Use a Conceptual Data Model?

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.

Benefits of Conceptual Data Models

Some of the key benefits of conceptual data modeling include:

Help Define a Common Language and Populate the Business Glossary

By providing a snapshot of concepts, entities and their relationships, gaps in terminology can be identified and oversights limited.

Help Shape the Roadmap and Define Project Scope

A conceptual model can help the relevant stakeholders better understand what is required to achieve their desired business outcomes from both a resource and time management perspective.

Provide a Basis for Future Models

Conceptual data modeling can be seen as the first step to more in-depth types of data modeling. Once the conceptual model is built, modelers can begin to introduce more context to the model. For a logical data model, that would be the data requirements of the database. For physical data modeling, the context would now include the specific database management system (DBMS) being modeled for.

Foster an Inclusive Form of Communication

Ensuring successful implementation requires stakeholder input from in and outside of the tech-bubble. Conceptual data models help foster this collaboration.

Create Better Data Models with erwin

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 (DM) also boasts an arsenal of powerful automation capabilities that speed up the process, reduce human errors, and increase efficiency.

erwin DM is a proven technology for improving the quality, accuracy and agility of an enterprise's data capabilities and supporting data governance and intelligence efforts. It's time to put your data to work.

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