Data modeling is a crucial step in structuring database models. It’s no secret that collecting data is essential for a business. But knowing how to structure it to perform a correct analysis of the information is what truly makes the difference.
Systems created from data modeling will serve to store information, establish relationships between database elements, and analyze information according to company requirements. This process helps the business to find the most relevant insights for strategic decision-making and to create a functional database for the company.
In this article, learn what data modeling is, the importance of creating a data model, the steps in the process, and the types of modeling most commonly used by data architects. Read on!
What is data modeling?
Data modeling involves creating a simplified diagram designed to structure the storage and flow of information relevant to a company that wants to build a database. In other words, the professional responsible for the modeling will need to develop a visual diagram of how the data for that project or company will be stored in the system being developed.
This structure needs to include, for example, the formats that this data will have, the relationships they establish with each other, and other logical and functional aspects important for the objectives of creating the database. Thus, this process is done before the creation of the database system ( software, platform, tool, or application) and requires a requirements analysis to understand the purpose of the modeling and how the database will be used.
Therefore, the data model needs to be well-designed and contain the requirements specified by the client in an intelligent, functional, and strategic way for the organization.
It’s important to remember that there are no set rules about what a data model should look like, as it’s crucial to consider the unique needs of each company/project when designing the structure of this system.
Why is data modeling important?
As you may have noticed, data modeling is a crucial step in database development, as it’s the moment to understand the company’s requirements and objectives for structuring this system. Furthermore, this process is done to establish all the necessary attributes for the database to store relevant information that can generate valuable insights for the company.
Therefore, a well-structured data model enables clearer and more accurate data storage and analysis, avoiding duplicate, conflicting, and irrelevant information for the company.
The steps of data modeling
Data modeling follows several steps to actually arrive at a database system that is consistent with the company’s needs.
The process has four stages, which are:
- Requirements analysis;
- Conceptual modeling;
- Logical modeling;
- Physical modeling.
See more details about each of them below.
Requirements analysis
The requirements analysis phase is essential for defining the project’s business rules. That is, what the client seeks from the creation of the system (software, platform, application, etc.). This phase is fundamental for understanding the client’s needs and the project requirements, and for specifying, analyzing, and validating them before proceeding to the creation of the database model.
Therefore, it is important to clarify with the client all the information you need to proceed with creating the data model.
Conceptual modeling
The conceptual data modeling phase aims to capture the requirements presented by stakeholders in the preceding phase and organize them with a business perspective.
The diagram created from the conceptual modeling should contain all the business rules established in the requirements analysis phase. In other words, the functionalities of this system. This phase is usually done with the client and contains some essential elements for the system to function.
The conceptual modeling diagram needs to have four elements, which are:
- Entities;
- Relationship;
- Cardinality;
- Attributes.
These elements will establish what the database system will contain (entities), how they relate within that system, the type/quality of the relationship between them (cardinality), and the attributes (characteristics) of these entities. This visual diagram will help bring the business vision of the project and will serve as a basis for the next steps in modeling a database system.
Logical data modeling (LDMs)
Logical data modeling utilizes the structure of the conceptual model and adds other information that ensures the logic of the system being developed.
In this step, elements such as entities, relationships, and attributes are used, in addition to the addition of keys. Keys are divided into primary and foreign keys. Primary keys (PK) are responsible for ensuring that the added data is unique and exclusive within the system.
This element within a database cannot be repeated or counted as null. This ensures that the information is reliable and prevents the insertion of duplicate data.
Foreign keys (FKs), on the other hand, are associative keys. Their function is to establish relationships between the entities of a system. Thus, it is possible to identify how these elements relate within the database.
For example, consider the entities “customer identification” and “product identification”. In modeling this system, each customer will have a primary key that identifies them as a unique element in this database. In this scenario, in the “product identification” entity, you can find the foreign key (FK), which represents a specific customer from the “customer identification” entity in the “product identification” entity.
These definitions are important for maintaining the logic and functionality of the data model being developed.
Physical data modeling (MFDs)
Physical data modeling is the most technical step in this process, where the data architect transforms the logical data model into a physical model.
In other words, this step involves creating the database itself, respecting the business rules defined in the previous steps, and following the requirements of the other models created previously.
A physical data model needs to be read by a DBMS (Database Management System). To make this possible, a development language (such as SQL ) is used to create the necessary structure for the DBMS to read the data model.
What are the types of data modeling?
There are two types of data modeling: relational modeling and dimensional modeling.
These modeling techniques serve different purposes. Check out the main characteristics of these types of data modeling:
Relational modeling
In a relational modeling system, the main characteristic is the ability to establish a relationship between the entities in the database.
This model is built from tables containing entities and fields with various attributes. For example, the entity ” customer” and the entity ” orders”. The idea is that the designed model can establish the relationship between these entities.
In the example given, the entity “customer ” could be related to the entity ” orders ” through the relationship “a customer can place orders” or “orders can be placed by a customer”.
The relational model is used to store data in an organization’s transactional systems, that is, those used in the day-to-day operations of a business, and which change, such as data insertions, modifications, and deletions.
Thus, the main objective of relational modeling is to store transactional business information in the database and make it accessible in these operational activities.
This type of modeling typically uses relational DBMSs, which are suitable for transactional systems as mentioned above.
Dimensional modeling
Dimensional modeling, on the other hand, is more commonly used for Data Warehouse (DW) and Business Intelligence (BI) processes. Therefore, the main objective of this model is to simplify the analysis of multidimensional data.
The dimensional model uses a fact table, which contains information on measures, for example, the quantity of sales, and dimension tables, where the entities are located, such as customers, date, category, product, etc., that are related to the fact table.
This model is most often used for data extraction, aggregation, and analysis. With it, it’s possible to generate reports, dashboards, and insights from these databases, which will aid in strategic business decision-making.
Therefore, this type of database modeling is more related to areas such as Business Intelligence (BI), which collects and analyzes data to aid in the company’s strategic planning.
Conclusion
Data modeling is a fundamental step in creating a database system. This process allows you to create the database structure for a company or project to achieve business objectives through data storage.
Therefore, the data model must be well-designed to avoid duplicate information, excessive data storage, and data conflicts in analyses.
This process is the foundation of Business Intelligence strategies and data analysis in general. Therefore, it is indispensable for companies that plan their strategies based on real and relevant data. So, if you are considering developing a product, creating a company, or undertaking a project that requires data collection, data modeling is an essential step to do it correctly for your business.