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Erwin Conceptual Data Model4/23/2021
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.Technical Support Submit a Ticket 1 813-773-4170 Chat Get live sales support.
![]() Conceptual Data Modeling Get the big-picture perspective of your organizations 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 arent necessarily tech- andor 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) andor 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. ![]() Its considered a best practice to distinguish between the as-is and to-be states via color scheme. ![]() 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.
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