Different Types of Data Models Explained with Examples

Different Types of Data Models Explained with Examples

data modeling

In the modern world, data is everything and everywhere. With so much access to technology, data has become a valuable resource for any business. Albeit a complex one. Data is any sort of facts, figures, and information that can be analyzed to guide future decision-making. But figuring out how to analyze and make sense of that data can be a real struggle at times. Because data has become so valuable one concept that has taken hold is data models. But what is a data model? In this article, we will take a deep dive into the different types of data modeling and look at some of the advantages of each type.

Here is a complete guide for understanding different types of data models.

What is a Data Model?

Before we get into specific types and forms, we first need to understand what a data model is. Put simply, data modeling is the process of organizing and compiling data into one uniform structure or form. With a data model, you can classify or categorize real-life elements into more easily understandable data sets. The ultimate goal of a data set is to manage data in a way in which the relationships between data elements become more clear and usable. Data modeling is especially necessary for businesses in which multiple people, departments, or branches need to be able to analyze and access data. Establishing a solid data model ensures the ability to easily transfer, access, and understand data from multiple different viewpoints.

If a data model is set up efficiently it leads to fewer backlogs, crashes, or miscues for the end user. With a well-constructed data model, businesses can save time and resources and provide a better product or service. So, what are the different types of data modeling?


We will first cover relational data models. Typically relational data models are organized into tables or charts organized into rows and columns. In a relational data model, the rows represent different attributes or related types of data. The columns represent the values, figures, or traits that make up the attribute in each row. For example, if you were recording temperatures in a specific location over the course of one year, each row could contain the month and the columns would contain average temperatures during each month. This is a very simplistic answer, but more complex relational data models can have multiple columns of information for each attribute row. The technical term for the data represented in each column is known as the domain.

Some of the biggest advantages of relational data models are simplicity and scalability. Of the three main kinds of data models, relation models are typically the easiest for people to comprehend and build. They are also extremely easy to scale up or down as your company grows and shifts. You just have to add or remove a row or column as needed. The downside of a relational model is that comparatively, it is rather slow to use and analyze when dealing with large amounts of data.


Next up, let’s talk about hierarchical data modeling. A hierarchical data model looks something like a flow chart or family tree. The parent, or “root node” as it is technically referred to, has multiple “child nodes” that stem off of it. Each child node has one singular root node. Root nodes however can all have multiple children that stem from them. For example, a hierarchical data model with the root node of “Sports” could have “Baseball”, “Basketball”, and “Football” as children that link to it. While “Sports” serves as the root node of each child, each child can only have one parent node. The children can then continue to branch off similarly. “Baseball” can branch off into more nodes such as “Major League”, “Minor League”, and “Recreational”.

The biggest benefits of using a hierarchical data model are that they are typically easy to use and understand, and they excel at simplifying complex data points. The downside of using a hierarchical data model is its limited flexibility. Unlike relational data models, it is much more difficult to adjust and adapt a hierarchical model.


Last but certainly not least is network data modeling. Network data models are often very similar to hierarchical data models but with one key difference. Unlike hierarchical models, children in the network model can have multiple root nodes, or as they are referred to in network models, “owners”. Network models are typically viewed as a natural evolution of hierarchical models as they allow significantly more flexibility. Going back to our “Sports” model, each child can now have multiple “owners”. Rather than classifying “Baseball” under just the “Sports” tab, it could now be categorized under “Non-Contact” and “Sports” at the same time. On the other side of things “Football” could be classified under “Contact” and “Sports”.

Typically network data models are some of the easiest to design and implement effectively. They tend to be quick to build and easier to understand for beginners. The problem with network models is once again their scalability. Network models can become very complex and difficult to interpret when too much data is collected.

All in all, data models are a crucial part of any successful modern business or organization. Hopefully, this article provided some insight on the subject and some guidance on which data model best suits your needs.


About Our Editorial Process

At DevX, we’re dedicated to tech entrepreneurship. Our team closely follows industry shifts, new products, AI breakthroughs, technology trends, and funding announcements. Articles undergo thorough editing to ensure accuracy and clarity, reflecting DevX’s style and supporting entrepreneurs in the tech sphere.

See our full editorial policy.

About Our Journalist