What Is a Data Model?

Written by Coursera Staff • Updated on

A data model helps data analysts and database architects prepare data for analysis to gain insight into strategic company decisions. Learn the data model categories and their role in data analytics.

[Featured image] A data scientist works on a data model on a tablet.

Key takeaways

A data model maps the relationships between data points, making it a useful tool for software development, data migration, and system integration.

  • Data models help transfer raw data into actionable insights.

  • The three types of data models are physical, logical, and conceptual.

Learn more about data models, including the types, uses, and data modeling techniques. If you’d like to begin increasing your data analytics skill set, enroll in the Google Data Analytics Professional Certificate, where in as little as six months you can learn about data visualization, Tableau software, R programming, and more.

What is a data model?

A data model defines the relationships between data points, useful in software development, data migration, and system integration. Data models can be used to design new systems and software. In business intelligence, they can help database architects and data analysts gain insight into company data and its attributes, and set parameters for data grouping, sorting, storing, and formatting.

The purpose of a data model is to help communicate the computer system’s requirements, interactions with the data, and potential outcomes. Building a data model specific to the needs of each individual organization helps reduce errors, encourage consistency, save time, improve database performance, and develop communication between teams.

Types of data models

Data models can be classified into three main categories based on the data’s abstraction levels (from lowest to highest abstraction):

  1. Physical: A physical data model provides a specific blueprint of how the database will store data, helping technicians accurately create the design.

  2. Logical: A logical data model omits technical specifications but includes essential details, such as data attributes, relationships, constraints, and subtypes. They can be applied to more than one database or system, meaning you could replicate the model for various products.

  3. Conceptual: A conceptual data model offers a big-picture view of the system’s content and structure. It can also serve as a starting point for designing logical and physical models.

What is data modeling?

Data modeling helps analysts visualize data and set parameters to gain insights that enable making strategic business decisions. It also helps incorporate formulas, currencies, and data hierarchy for easier manipulation. Data models transform raw data into actionable insights.

Data modeling examples and techniques

You can use data models to help determine how you will store your data within a database. The database model you choose will depend partially on the needs of your business and the type of data, and it will provide a structure for how you store that data and how the data points relate to each other. For example, a common data model is a relational database, where your data is stored in tables and each row and column of data is related in a labelled way. You might use a relational database to store information about your contacts, such as first name, last name, phone number, email address, and social media handle. 

Another example of a data model is a hierarchical data model, where data is stored under broader parent categories. For example, a family tree is a hierarchical data model where each generation is visualized under the last in a wide pyramid shape.

Read more: 8 Popular Database Management Tools

Related terms

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