There are three stages of data modeling, and three types of data model – 1. Conceptual, 2. Logical and 3. Physical – each of which plays a distinct role in the data modeling process. They help organization’s efforts in organizing, understanding, and making productive use of enterprise data resources.
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The three types of data model are conceptual data models, logical data models and physical data models. Each type of data model conveys the same information, from different perspectives. In order to create well-rounded and comprehensive models, the different types of data model help address different stakeholders needs and levels of expertise.
Conceptual, logical, and physical data models address the use of data assets from different degrees of abstraction. The models increase in complexity – starting with conceptual, through to physical data models. The models are used in different stages of the development process to foster the alignment of business goals and requirements with how data resources are used.
are used to communicate business structures and concepts at a high level of abstraction. These models are constructed without taking system constraints into account and are usually developed by business stakeholders and data architects to define and organize the information that is needed to develop a system.
are concerned with the types, attributes, and relationships of the entities that will inhabit the system. A logical model is often created by a data architect and used by business analysts. The goal is to develop a platform-independent representation of the entities and their relationships. This stage of data modeling provides organizations with insight pertaining to the limitations of their current technologies.
are used to define the implementation of logical data models employing a particular database management system (DBMS). They are built with the current – or expected (as is/to be) – technological capabilities. Database developers and analysts work with physical data models to enact the ideas and processes refined by conceptual and logical models.
Concepts and system requirements are refined at each step as they move from conceptual models to logical models and are solidified in physical models.
When done right, data modeling adds value to the modern enterprise. But to maximize that value, organizations need a solid understanding of the types of data model, and stages of data modeling. Different types of data models can be used to address the diverse audiences involved in the development of new database systems.
It is important to use the correct type of data model to engage the business stakeholders involved in different stages of system development. The wrong type of data model will not convey the desired information to the intended audience nor be as useful in outlining and defining system requirements.
The initiative for a new database or application often comes from stakeholders trying to address evolving business requirements. These teams or individuals are interested in resolving a problem or streamlining a process from a very high-level perspective. The details of how the requirements will be met are irrelevant and will be handled by the appropriate technical parties.
Conceptual data models are the right vehicle for describing the purpose and components of the new system. A well-constructed conceptual model provides decision-makers with the information they need to approve or reject the proposed system.
Logical data models are then constructed by data architects in consultation with business analysts who understand the goals outlined in a conceptual model. At this level of abstraction, it becomes important to have a good understanding of the entities that will be represented in the system.
The purpose of logical models is to fully define the attributes and relationships exhibited by the identified entities. Ideally, this should be done while maintaining flexibility as to the database platform used for implementation. During the development and refinement of a logical model, it may become apparent that a particular database solution should be used for its implementation.
Physical data models turn the initial abstract concepts regarding the entities involved in the system into schemas that target a specific database platform. They are used by the teams responsible for implementing the ideas and creating viable systems to address business requirements.
Physical data models are necessarily the most technical type of model. Concepts that are easily communicated abstractly can be technically challenging to implement. Keeping this sensitive data safe is simply stated in a conceptual model, but not so easily laid out when considering its physical implementation.
IDERA’s ER/Studio suite of data modeling tools enables teams to collaborate in the creation of conceptual, logical, and physical entity-relationship data models. The tools allow models to be created at the required level of abstraction to communicate with a specific audience.
Business Architect provides teams with a tool for creating conceptual data models to facilitate designing processes aligned with business goals and requirements. Create models to map relationships between people, processes, and data. The conceptual models can then be exported to ER/Studio Data Architect for the creation of logical data models.
Data Architect lets teams build and manage entity-relationship (ER) data models to streamline and enhance the database development cycle. The tool can discover and document existing data assets across the computing environment so they can be appropriately addressed. Impact analysis can be performed to ensure that new policies or changes to data models, databases, or data fields don’t clash with business requirements and expectations.
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Enterprise Team Edition is IDERA’s solution for collaborative data modeling. Combined with automated business glossary management, the tool’s collaborative features help build a foundation for a data governance program, and supports organization in discovering and documenting existing enterprise data assets. With the documentation of data assets and creation of global business glossaries & data definitions, organizations can implement effective and consistent enterprise-wide data governance.
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The three core types are:
Conceptual models – high-level business concepts and relationships
Logical models – detailed attributes and data structures, platform-independent
Physical models – concrete implementation details for a specific database platform
Each type of model communicates with a different audience. Conceptual models align with business needs, logical models clarify structure for analysts, and physical models guide implementation for DBAs.
You can—but it’s risky. Skipping conceptual or logical modeling often leads to misalignment, costly rework, or systems that don’t meet business requirements.
ER/Studio lets you build, connect, and evolve conceptual, logical, and physical models in one suite—ensuring continuity, consistency, and collaboration across every stage.
Data models—especially when built collaboratively—create a shared structure and language around data. They help define ownership, standardize terminology, and ensure data is used consistently across systems, which is critical for governance and compliance.
Yes. With ER/Studio, conceptual and logical models can be reused, extended, and adapted across projects and platforms. This reduces rework, enforces standards, and accelerates delivery while keeping your models aligned with evolving business goals.
