Conceptual data modeling is one of three types of data modeling - also referred to as stages of data modeling. The process describes the creation of conceptual data models, and it helps organizations conceptualize databases in order to build them more efficiently and comprehensively.
When done in line with data modeling best practices, organizations create conceptual models before developing them into logical, and then physical data models. This is achieved by adding new levels of abstraction and detail - that encompass different business requirements - at each stage.
A conceptual data model is the most abstract variety of data models.
A popular method of data modeling uses the entity/relationship (ER) approach and encompasses the data models we will be discussing.
A conceptual data model can be defined as a high-level model that provides an in-depth view of business concepts. It is useful for identifying key business and system entities and establishing the relationships between them.
They are often designed to be platform-independent, leaving choices regarding the database management systems (DBMS) or storage methodology to be addressed in later stages of model development.
Figure 1: Conceptual Data Model Example
The platform-independent approach means that organizations can conceptualize outside of their current capabilities. Therefore, the conceptual modeling stage is an opportunity for organizations to plan the most perfect database to suit their needs. They can help identify any gaps in their current capabilities and/or tools and be used to justify investment.
A conceptual data model is created primarily for a business audience. It forms the first phase of a three-stage modeling strategy, and as its name implies, is most concerned with providing a vehicle for understanding the abstract concepts surrounding a business problem. Once the business concepts have been codified, more detail will be added as the data modeling process continues.
After the conceptual data model stage, teams take the information captured within the conceptual data model and proceed with the creation of less abstract and more concrete data models. Best practices take the conceptual model and use it as the basis for a logical data model. In turn, the logical data model informs the physical.
Obtaining the greatest benefits from data modeling requires a linear progression through all three stages of data modeling. Starting with a conceptual model is the most reliable way to ensure the completed database solution meets business requirements.
A conceptual data model frees organizations from the constraints of their current capabilities, allowing them to dream big. They allow teams to think outside the box and come up with innovative, and the best possible ways to address the needs of the business. Conceptual models can be instrumental in finding ways to improve current system capabilities.
Going directly to a logical or physical data model risks missing important concepts or relationships necessary for a system to meet the underlying business requirements for which it is being developed. It is much more difficult to make changes once a physical model is constructed than it is to clarify the requirements fully in a conceptual model.
Building a conceptual data model begins with understanding how business requirements can be addressed in an information technology (IT) system.
The first step in building a conceptual data model is to gather the business requirements that the prospective system is designed to address. This can be done by interviewing stakeholders, consulting business documents, and working with business analysts. The information collected is restricted to high-level constructs such as the entities that will populate the model and their relationships to each other.
Conceptual models do not include granular information such as table structure, data types, or keys. These critical aspects of database development are left until later modeling stages. The platform on which the database will be implemented, storage techniques, and hardware considerations are not part of a conceptual data model.
Conceptual data models can be constructed without dedicated tools, though there are some disadvantages to this strategy.
For starters, collaboration without dedicated tools can be difficult. Logistically, sharing the information between parties is more difficult using this approach. However, the lack of formality and common understanding this approach can involve is perhaps an even bigger hurdle to effective and collaborative data modeling.
Additionally, trying to keep track of the components of a complex data model can be difficult and result in errors or misunderstandings that will negatively impact system development.
Without a dedicated data modeling tool, organizations will have to recreate models at each stage of data modeling, increasing the opportunity for error. Dedicated tools for data modeling offer the ability to progress models through each stage, limiting the need for recreating models and the potential for human error. As well as being more accurate, this approach speeds up the process.
IDERA’s ER/Studio suite of data modeling tools enables organizations to create conceptual, logical, and physical data models to streamline database development and assist in using information resources effectively. Three versions of the tool are available to provide the right solution for enterprise data modeling needs.
Business Architect enables the design of conceptual data models to align database development with an organization’s business objectives. It maps the relationships between people, processes, and data that are required for the construction of viable conceptual data models.
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ER/Studio Data Architect offers teams a valuable tool to document data resources and design new systems. It enables the creation and management of logical and physical models that can be used for database creation or to support a data governance initiative.
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ER/Studio Enterprise Edition is a versatile platform that fosters collaboration in the data modeling process. Consistency is maintained between data models by keeping all stakeholders informed of changes through a shared repository.
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Organizations looking to improve their data modeling capabilities should look into adding ER/Studio to their software portfolio. The tools will help enterprises get the most out of their data resources.
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