What is Data Modeling?

Data modeling is a central step of software engineering. A data-driven company’s objectives are to evaluate all data dependencies, to explain how the data will be used by the software, and to define the data objects that will be stored in the database for later use. Are you wondering about what data modeling is, its founding principles, and the different types of models? Follow this guide:

The life cycle of data, while it may seem technically complex, is conceptually quite simple. First, you need to collect the data. Then you need to clean and organize it. Finally, you need to understand how you can use it. This crucial phase is based on data modeling. The idea is to create a visual representation of an entire data portfolio (or certain segments of the data) to easily identify the different types of data available, the relationships that may exist between these different types of data, and how they can be grouped, split up, or in any case organized to interact and generate value.

Data modeling, therefore, plays a key role in knowing how to exploit your data. Data models are built to meet the needs of the business. So, while there are different types of data models, one should never lose sight of the company’s objectives for data modeling to be truly effective.

Some of the advantages of data modeling include: reducing the risk of error during database software development, saving valuable time during the design and creation of databases, and ensuring consistency in the design of data systems. Data modeling also promises to simplify the communication between data and business teams.

The Different Types of Data Modeling

To get started on the path to data modeling, you need to start by knowing the main types of data models. Very schematically, there are three types of models:

The Conceptual Data Model

The conceptual data model gives context and helps teams understand the data outside of the technical dimension. The conceptual model is for everyone in your company, even those who lack technical skills. The conceptual model describes the data contained by the system, its attributes and data constraints, the business rules that govern the data, and the data security and integrity requirements.

The Logical Data Model

Logical models deliver more detail about the concepts and relationships in a data domain. In other words, they describe entities and attributes to provide a clear representation of the purpose of data for the business. A logical data model is a model that is not specific to a database. It describes the data in as much detail as possible, regardless of how it will be physically implemented in the database. Characteristics of a logical data model include all entities and the relationships between them, the attributes of each entity, and the primary key of each entity, for example.

The Physical Data Model

The physical data model represents how the model will be built in the database. A physical database model displays the entire table structures, including the column name, column data type, column constraints, primary key, foreign key, and relationships between tables. A physical data model will be used by database administrators to estimate the size of database systems and to perform capacity planning.

How Data Modeling Works

Data modeling is based on three key models: the relational model, the hierarchical model, and the entity-association model. The relational model is both the oldest and the most commonly used. It deals primarily with numerical data and is used mainly in mathematical calculations such as sums or averages. There is also the option to move towards a hierarchical model, which is optimized for online queries and data warehouse tools. In this case, the data is classified hierarchically, in a descending structure. Finally, there is the E-R model, which is used to generate a relational database in which each entry represents an entity and has fields that contain attributes.

Guarantee the integrity of your data, make the use of your data assets more reliable, and facilitate the development of a data culture within your company. Data modeling will allow you to be part of a virtuous circle of data use.

About Actian Corporation

Actian makes data easy. Our data platform simplifies how people connect, manage, and analyze data across cloud, hybrid, and on-premises environments. With decades of experience in data management and analytics, Actian delivers high-performance solutions that empower businesses to make data-driven decisions. Actian is recognized by leading analysts and has received industry awards for performance and innovation. Our teams share proven use cases at conferences (e.g., Strata Data) and contribute to open-source projects. On the Actian blog, we cover topics ranging from real-time data ingestion, data analytics, data governance, data management, data quality, data intelligence to AI-driven analytics.