Exploring Data Modeling Techniques: Popular Options for 2025

Businesses collect massive amounts of data in an effort to seek out important information that helps inform strategic decision-making. However, raw data isn’t the most useful form of data, and organizations need ways to successfully analyze and represent this data to find actionable insights. Data modeling uses visual representations to analyze and define all the different types of data your business might collect, turning it into a helpful resource. Your business can benefit from data modeling in several ways, such as encouraging collaboration, improving business processes, and saving time and money. Read on to learn more about data modeling, including data modeling techniques and the different types of data modeling tools you might use.

Types of data modeling techniques

When incorporating data modeling into your company’s operations, you typically have three techniques from which to choose: Conceptual, logical, and physical data modeling. Explore these three methods in more detail:

Conceptual data modeling

Often called domain models, conceptual data modeling offers an overall view of what a system contains, which rules exist, and how the system’s organization works. It helps to provide definition to the general framework of your business and your data. Conceptual data modeling labels the different data sets and demonstrates how it all flows through a business. It identifies the different entities—what to represent in the data model—and how they relate to each other.

Logical data modeling

A logical data model takes the foundation of the conceptual data model and builds on it by assigning specific details to each entity and relationship. A formal notation system helps to provide information that’s not typically included in a more abstract model. Although logical data models illustrate data flow and database content, they typically don’t include any database specifications since they are meant for application across a variety of products.

Physical data modeling

Physical data models build on the logical data model by applying a specific database or program to the model itself. Essentially, you construct the physical data model with a specific database management system (DBMS) in mind. The physical data model is the culmination of all the other models and tailors specifically to a certain database tool and data storage technology. Physical data models help professionals identify whether they have every aspect of the database plan covered before implementing it across an organization.

Popular data modeling techniques in 2025

An effective data model supports analysis and any decisions derived from it, and a few popular techniques you can employ are graph data modeling, machine learning models, and dimensional data modeling. You can review some of these data modeling techniques below:

• Graph data modeling: Uses nodes to represent entities and edges to demonstrate the relationships between them.

• Machine learning models: Uses algorithms to learn from and then analyze a data set.

• Data vault modeling: Uses hubs, links, and satellites to represent core business concepts and their relationships for analytics on an enterprise-level scale.

• Dimensional data modeling: Creates a staging layer that organizes data by facts and dimensions, allowing for easier access to the data.

• Multi-model data modeling: Supports multiple types of models, such as documents, object-oriented, full text, and graphs, at the same time.

• NoSQL data modeling: Stores both semi-structured and unstructured data in different types of models.

• Automated dimensional modeling: Automatically stores, organizes, and recognizes the structure of your data.

What are data modeling techniques used for?

Data modeling techniques are necessary for building any sort of data infrastructure within your organization. They are key to creating a blueprint that can inform you and your business regarding the most effective method for storing and managing your data. Data modeling techniques can help identify the type of data a business has, how best to utilize it, and what type of infrastructure suits that data for storage and management.

Who uses data modeling techniques?

A wide range of data professionals, such as data scientists and database administrators, use data modeling techniques to develop and implement new structures. Some of these roles include:

Database architects

Median annual salary: $115,496 [1]

Database architects use data modeling to design large-scale databases for storing, managing, and analyzing data sets. In this role, you would analyze an organization's specific needs and make recommendations about the database design to better meet those needs.

Database administrators

Median annual salary: $105,004 [2]

Database administrators create new databases and upgrade old ones to ensure data is secure, searchable, and operational. As a database administrator, you use software expertise to store and organize data in the most effective way possible and work with security professionals to minimize any risk of cyberattacks.

Data analysts and scientists

Median annual salary: $116,600 [3]

Data analysts and scientists use the information within a data set as research, seeking insights that help to increase an organization’s profits, maximize growth, and create more efficient processes. You will access the data within a data modeling structure and use it to create actionable reports that other non-data professionals can understand.

Software engineers

Median annual salary: $117,399 [4]

Software engineers use coding and programming languages to design, manage, monitor, and update software. You create programs designed to address a wide range of different functions based on an organization’s specific goals and needs.

Pros and cons of using data modeling techniques

Data modeling techniques allow you to document, utilize, and manage your data in a variety of ways. Some of the benefits and drawbacks of using data modeling techniques include:

Benefits

The right data modeling technique for your business helps to reduce errors within the data while also enhancing its integrity. It helps you to better understand the type of data you have and what tools or resources are the most effective ones for that data’s storage, management, and utilization. It increases consistency across an organization’s systems and improves its performance. Data modeling techniques also increase the speed of data retrieval, which better supports data professionals in their roles.

Drawbacks

One drawback of data modeling techniques is that choosing the wrong one for your business risks not uncovering the right insights. Additionally, you need the right technique to ensure the data model fulfills its requirements well. It’s also a potentially time-consuming and expensive undertaking to implement the software or databases required for data modeling techniques.

How to get started in data modeling

If you’re interested in data modeling, you’ll first want to explore the field and see if it fits your interests and lifestyle. Data modelers and other data professionals typically need to prepare to continuously educate themselves on current and growing trends. You’ll most likely need a four-year degree in a subject such as computer science or information technology. You need to have a strong background in software engineering, mathematics, and database design. An entry-level job or other opportunities that allow you to learn these skills in a practical way are key to growing and developing within the industry.

Learn more about data modeling techniques on Coursera

Data modeling techniques are specific types of visual structures that help an organization to better document and utilize the data they collect. If you’re interested in data modeling techniques, explore the wide range of courses and Professional Certificates on Coursera. With options such as the University of Colorado Boulder’s Fundamental Tools of Data Wrangling, you’ll have the chance to discover more about the foundations of a data modeling career.