Types of Data Models: Conceptual, Logical, Physical

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 Different Types of Data Model

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. 

  • Conceptual data models 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. 

  • Logical data models 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.

  • Physical data models 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. 

Choosing the Right Type of Data Model

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. 

Conceptual Data Models

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.

Types of Data Model - Conceptual

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.

See Also: What Is Conceptual Data Modeling? (And When Should It Be Used?)

Logical Data Models

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.

Types of Data Model - Logical

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.

See Also: What Is Logical Data Modeling? (And Why Is It Important?)

Physical Data Models

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.

Types of Data Model - Physical Models 

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.

See Also: What Is Physical Data Modeling? (and When Should It Be Used?)

How to Build Data Models

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. 

Create Conceptual Models with ER/Studio Business Architect

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. 

Create Logical Data Models with ER/Studio Data Architect

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.

Collaborative Data Modeling with ER/Studio Enterprise Team Edition

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. 

These excellent data modeling solutions can be evaluated with a free 14-day trial that does not require a credit card and provides access to the tools’ full slate of features. 

Useful Data Modeling Resources:

ER/Studio Business Architect Datasheet
Everything you need to know, all in one downloadable PDF. DOWNLOAD THE DATASHEET

ER/Studio Data Architect Datasheet
Everything you need to know, all in one downloadable PDF. DOWNLOAD THE DATASHEET

ER/Studio Enterprise Team Edition Datasheet
Everything you need to know, all in one downloadable PDF. DOWNLOAD THE DATASHEET

See all data modeling resources.

Five Data Modeling Tips for Data Governance



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