Data modeling: definition, advantages and types

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Over the years, companies have had access to larger amounts of data obtained through various sources. Interest in the use of this data has generated a need for companies to implement tools and methodologies that allow them to access it in an orderly and structured way, with the aim of analyzing, understanding and applying it in decision-making processes regarding businesses, products or services.

Data analysis is a process that requires the collection, how does botim work? storage and processing of data in an effective and efficient manner. Compliance with these requirements is achieved through the implementation and development of data modeling .

Data modeling is the process that will allow companies to analyze and understand their data, transforming it into valuable information that contributes to the evolution of their businesses.

If you are interested in learning more about data modeling , what it is, what its advantages are, and what types of data models exist, keep reading this article, where you can learn more about these topics and find out where to study data analysis so that you can become an expert in this field.

Definition of data modeling
Data modeling refers to the process of diagramming data flows, that is, creating the scheme that will define the way in which a company's data and information are collected and managed.

The scheme defined by data modeling allows data sets to be structured and organized so that they can ultimately be processed easily and correctly through a database.

The goal, then, of data modeling is to take a group or set of data that is not structured or organized and give it a systematic and clear order that provides analysts and executives with efficient and effective treatment and understanding of the information.

Advantages of data modeling
Data modeling provides companies with benefits that not only contribute to commercial areas, but also become the roadmap to optimize processes ranging from the choice of technologies for database design, to quality control, performance improvement and business investment.

Below are some of the advantages of data modeling :

First, data modeling gives companies the ability to view data as valuable information that is organized, clean, and easy to understand.
The data modeling process enables the identification and reduction of errors in the design and development of database software, as well as optimizing data integrity.
Supports the identification of repeated, redundant and unnecessary data, as well as the indication of possible data gaps during the modeling process.
Ensures consistency in the design of a company's system as well as in the documentation of its data.
Improve communication between data scientists, engineers, developers, and business intelligence professionals and areas.
It fosters scenarios and structures for collaboration and teamwork between IT professionals and the commercial areas of a company.
Types of data models
The type of data model implemented in the data modeling process will determine the logical configuration of the data; that is, it defines the way in which data will be stored, organized, and retrieved in a database.

There are various types of data models , however, three main ones stand out:

Conceptual data models: can be defined as the starting point of data modeling . Through this model, the general structure and content of a project are established, without details. Its objective is to organize business concepts, according to the needs of the company and the data scientists and engineers. The conceptual data model acts as the plan or roadmap for the implementation of the logical and physical models.
Conceptual data models expand the picture and provide insights into:

The data that the system contains.
The attributes that the data possesses, its conditions and restrictions.
Business rules that link to data.
Forms and alternatives for better data organization.
Requirements to ensure data security and integrity.


Logical data models: This is considered a second level focused on detail, and addresses the data modeling process in greater depth . Its objective is to provide a large amount of detail about the data, its concepts, and the data relationships that can be identified in the conceptual data model . Within the logical data modeling process, the following are defined:
The attributes or key fields of the data.
Primary or main keys.
Foreign keys, also known as foreign or alien keys.
Cardinality, which refers to the number of elements that are part of the relationship between two or more data entities.
The definition of units or sets of information and their classes.


Physical data models: This is the last phase of the data modeling scheme and focuses on defining which technologies, hardware and software, will be used to create the actual database structure. The physical data model provides a reliable preview of how the database system design will be implemented in a company's organization.

The physical data model is a stage where details such as:
The types of data fields that are displayed in the Database Management System, that is, the tables and columns of the database are specified.
The identification of data relationships represented in the tables of the Database Management System.
Additional aspects, including performance adjustments.