The practice of developing a basic picture of a complicated software system using text and symbols to illustrate how data will flow is known as data modeling. The graphic can be used as a blueprint for building new software or reengineering an existing program to ensure efficient data consumption.
There are three different types of data models – conceptual, logical, and physical. In this article, we will focus only on conceptual data models and logical data models.
Conceptual data model
The initial stage of the data modeling process is to create conceptual data models. They offer a high-level overview, skipping finer details in favor of a more digestible format. When an organization writes a basic strategy to work out the finer details later, conceptual data modeling is most useful. Conceptual data models, which are typically built by data architects and business stakeholders, provide stakeholders with an easily digestible picture of the important concepts or entities, as well as the relationships between them.
Logical data model
Logical data modeling is the second stage of data modeling which is also known as information modeling. It assists companies in visualizing the data they must process to successfully execute specified activities or business processes. The data is described in as much detail as feasible in the logical data model, regardless of how it will be physically represented in the database.
Some differences between the conceptual data model and the logical data model are given below:
Entities and relationships are represented in a conceptual data model. The most significant items and their relationships are represented in the conceptual data model. The logical data model more accurately describes the data than the conceptual model. It is not, however, utilized to create a genuine database. All entities, relationships, and attributes are included. These attributes describe an entity’s traits or properties. A primary key and a foreign key are also included in a logical data model. It is also likely to use normalization.
Complexity level increases as we go from conceptual to logical to physical. We can observe how things get more complicated in each stage. This is why we always begin with the conceptual data model so that we can gain a high-level understanding of the many entities in our data and how they interact with each other, then move on to the logical data model so that we can understand the details of our data without worrying about how they will be executed or enforced.
The conceptual data model does not characterize attributes whereas a logical data model characterizes attributes.
Conceptual data models do not establish primary keys and foreign keys, whereas logical data models establish primary keys and foreign keys.
The conceptual data model is the foundation for developing the logical data model, whereas the logical data model is the foundation for developing the physical data model.
In conclusion, data modelling is important because it facilitates the integration of high-level business processes.