Skip to content
Dimension vs Measure in Tableau, Explained.

Understanding Dimensions and Measures in Tableau

Updated on

Tableau is a leading data visualization tool that helps users create interactive and insightful visualizations. It offers a comprehensive suite of features and capabilities, making it an ideal choice for professionals working with data analytics. In this article, we'll explore the differences between dimensions and measures in Tableau, two fundamental concepts that play a crucial role in building effective visualizations.

Looking for an open source data visualization tool that works as the Tableau Alternative? You might want to take a look at RATH (opens in a new tab). You can easily create charts by dragging and dropping variables with a tableau-alternative interface with ease:

RATH is Open Source. Check out RATH GitHub Page (opens in a new tab) for more details!

Tableau Dimensions

Dimensions are the fields in Tableau that help you split, categorize, and organize your data. They are the independent variables, usually qualitative or categorical in nature, and are used to slice and dice the data for better analyses. Examples of dimensions include Order ID, employee IDs, and region.

In Tableau, dimensions are represented as blue pills and can be discrete or continuous fields. Discrete fields are individual and distinct values, whereas continuous fields are ranged values. Understanding the difference between discrete and continuous dimensions is essential for creating effective visualizations.

Discrete Dimensions

Discrete dimensions are fields that contain individual, distinct values. Examples of discrete dimensions include employee IDs, order IDs, and product categories. These dimensions help you understand the data on a granular level and enable you to split and organize the data.

Continuous Dimensions

Continuous dimensions are fields that contain ranged values, such as sales numbers or dates. These dimensions allow you to analyze trends and patterns over time or across a range of values.

Tableau Measures

Measures, on the other hand, are the fields in Tableau that contain quantitative or numeric information. They represent the dependent variables and are used to calculate aggregated values such as sum, average, and profit. Examples of measures include revenue, sales, and discounts.

In Tableau, measures are represented as green pills and, like dimensions, can be discrete or continuous fields. The primary difference between dimensions and measures lies in their aggregation, as measures are always aggregated into a single value.

Aggregation in Tableau

Aggregation is the process of combining multiple data points into a single value. In Tableau, measures are aggregated to provide a summarized view of the data. Common aggregation methods include sum, average, minimum, maximum, and count. The level of detail in a visualization can be adjusted by including or excluding dimensions, which affects the aggregation of measures.

Blue and Green Pills: Discrete and Continuous Fields

Tableau uses blue and green pills to visually represent discrete and continuous fields in the workspace. Blue pills represent discrete dimensions, while green pills represent continuous dimensions and measures. This visual distinction helps users understand the type of data they are working with and how it will affect the visualization.

Data Visualization and Analysis Tools

Tableau is just one of many data visualization and analysis tools available today. Other popular tools include Looker and Google Data Studio, each offering unique features and capabilities. However, regardless of the tool you choose, understanding the fundamentals of dimensions, measures, and their respective roles in data visualization is crucial for creating effective and insightful visualizations.


In conclusion, dimensions and measures are essential concepts in Tableau, as they form the foundation of data visualization and analysis. Dimensions are the independent variables that help categorize and organize the data, while measures are the dependent variables that provide the quantitative information needed to perform calculations and derive insights. By understanding the differences between dimensions and measures and how they interact, you can create powerful and insightful visualizations that help drive better decision-making.