Tableau Dimensions vs Measures

Difference between Dimensions and Measures in Tableau

In a dataset, there are two types of features available. They are continuous data and discrete data. In other words, they can also be considered as quantitative and qualitative features, respectively. The features providing qualitative data types are called “Dimensions.” Consequently, the quantitative data types are called “Measures.” These can be converted to discrete or continuous data types.

Measures are generally not aggregated when used in visualizations.

Measures Example

Dimensions are aggregated due to their continuous data type.

Dimension Example
Key Takeaways
  • Dimensions are utilized to categorize and organize data effectively, providing context and descriptive attributes for analysis.
  • They are essential for grouping data into meaningful segments, such as product categories, customer segments, or geographic regions.
  • Measures are critical for quantitative analysis, allowing users to analyze and compare numerical data effectively.
  • They are used for aggregating data using functions like sum, average, count, min, or max, providing summarized insights into the dataset.
  • Dimensions primarily contain categorical data, while measures contain numerical data.
  • Parameters can be used with both dimensions and measures to enable dynamic filtering and customization of visualizations.
  • Discrete dimensions/measures result in distinct categories, while continuous dimensions/measures represent a continuous range of values.

What is a Measure?

A data type providing a quantitative or numerical value can be categorized as a “Measure.” Measures are a core component of every data visualization technique. Without the numerical data required to create highly interactive graphs and dashboards, no data analysis can be done. Measures have different features, which are explained in detail below.

Data Type

  • Measures are categorized as features with Numerical data types, which means that they have a value that can be counted or used to perform mathematical calculations.
  • Some of the common “Measure” data types are “Sales,” “Profit,” and “Discount” from the Sample-Superstore dataset.

Statistic Aggregation

  • Measures are generally Aggregated using various statistical functions such as “SUM,” “AVG,” “MIN,” “MAX,” “CNT (Count),” and “CNTD (Count Distinct),” depending on your choice.
  • These choices impact the visualizations and provide different insights based on the statistic aggregation you have chosen.


  • Measures play an important role in providing the numeric data needed to build the graphs and dashboards.
  • The numerical values of Measures can be depicted using aggregates in a graph.


  • Using Measures, you can create custom calculations, which will assist you in creating graphical representations.
  • Tableau provides many functions and categorizations to assist in creating customizable KPIs (Key Performance Indicators).

Examples Of A Measure

  • In the case of the Sample-Superstore dataset, the “Measure Values” are as shown.
Tableau Dimensions vs Measures - Measures
  • The features, Orders, Discount, Profit, Quantity and Sales are the “Measures” of the dataset.

What is a Dimension?

A feature in a dataset that provides qualitative or non-numerical values such as dates, names, and locations is called a dimension. You cannot just use numbers to provide a visualization. For that, context is needed. It is where the qualitative values of a Dimension shine through. They describe the numerical values held by the measures.

Data Type

  • Dimensions are generally non-numeric and can hold various data types, from character arrays to dates to geographical values such as Cities and Countries.
  • Some of the common “Dimensions” are “Country,” “Name,” “Customer ID,” and so on. 


  • Dimensions can be used to differentiate the Measures into different categories for data analysis.
  • These categories can be differentiated using colors or different visualizations to compare and contrast between two or more dimensions.


  • Used to represent Categorical data aside from graphs and more.
  • Tables can be built, and values can be measured separately using Dimensions.


  • Dimensions can be grouped into hierarchies, with specific features having less priority over the other.
  • For example, the Country has the highest hierarchy; below that comes the State, and then comes the name of a city in a dataset.

Examples Of Dimension

  • In the case of the Sample-Superstore dataset, the “Dimensions” are as shown.
Tableau Dimensions vs Measures - Dimension
  • “City,” “Country,” “State,” “Customer ID,” “Region,” and “Segment” are some of the various Dimensions available in the dataset.

Dimensions vs. Measures in Tableau: Key Differences

Tableau shows a few noticeable differences between a dimension and a measure. These differences are gone over in detail in the Tableau dimensions vs measures differences below. 

Nature of DataDimensions have qualitative, categorical attributes or descriptors. They categorize and classify the numeric data (Measures) based on various characteristics.Measures provide quantitative, numerical values that can be measured and aggregated using statistical functions. They provide the numeric metrics for analysis and calculation of the Qualitative features (Dimensions).
Usage and PurposeDimensions are used for categorization, grouping, and filtering data. They provide context and descriptive attributes for the analysis of the numeric data (e.g., the state has numeric coordinates).Measures are used for quantitative analysis, aggregation, and calculation. They represent the numeric metrics that are being analyzed using statistical functions.
AggregationDimensions aren’t aggregated. They merely provide the levels used to perform aggregations for the Measures.Measures are aggregates using statistical functions such as “SUM,” “AVG,” “MIN,” “MAX,” “CNT (Count),” and “CNTD (Count Distinct).”
RepresentationDimensions are generally represented by categorical axes in visualizations, such as the x-axis or the color legend in a chart. These features determine how the data is segmented or grouped in the visualization.Measures are represented by quantitative axes in visualizations, such as the y-axis in a bar chart or the size of bubbles in a bubble chart. They determine the numeric values being visualized and analyzed.
Hierarchical StructureSome dimensions may have hierarchical structures consisting of levels or subcategories. For example, a date dimension may have levels like year, quarter, month, and day.Measures do not have any hierarchical structure. They simply provide numerical values.
Calculation and AnalysisDimensions are used for grouping and filtering data. They do not undergo mathematical operations directly but can be used to define conditions or criteria for calculations.Measures are usually calculated using mathematical or statistical functions for analysis and insights. These can be used to predict trends, compare and contrast, and so on.

Conversion of Dimensions and Measures in Tableau

In some cases, for some analysis, there is a need to interchange some of the features’ functionalities from qualitative to quantitative and so on. Read more on how to do the conversion easily.

Converting Dimension to Measure

Follow these easy steps on how to convert a feature from a dimension to a measure.

Step 1: To convert a Dimension to a Measure, first drag and drop a dimension to the Row/Column component.

Convert Dimension - Step 1

Step 2: Right-click on the “City” and click on “Measure” in the drop-down. Select any statistical measure of your choice. Here, the “COUNT” statistical value is chosen.

Tableau Dimensions vs Measures - Convert Dimension - Step 2

Step 3: For a better understanding, drag this converted measure to the “Text” component in the “Marks” tab.

Tableau Dimensions vs Measures - Convert Dimension - Step 3

It is the value from the dimension once it is converted to a measure.

Tableau Dimensions vs Measures - Convert Dimension - Step 3 - Value

Converting Measure to Dimension

Follow the steps below to learn how to convert a Measure to a Dimension. Here, a calculated field is used to categorize the Measure values into a custom category.

Step 1: Create a calculated field.

Convert Measures - Step 1

Step 2: Create categories for ranges of sales made using the IF condition.

Tableau Dimensions vs Measures - Convert Measures - Step 2

Step 3: Apply these categories with the calculated field in the visualization.

Convert Measures - Step 3

Step 4: Drag and drop the calculated field in the “Text” box in the “Marks” tab.

Convert Measures - Step 4

It results in a Measure being converted to a category and subsequently, a Dimension.

Tableau Dimensions vs Measures - Convert Measures - Step 4 - value

Uses of Dimensions and Measures in Tableau 


Dimensions are an integral part of Tableau. Here are its primary uses.

  • Grouping and Categorization: Dimensions are used for grouping and categorizing data. You are able to segment your data into meaningful groups from categorical attributes such as product segments, or geographic locations.
  • Filtering: Dimensions can be used to filter data by specific criteria. You can filter data to focus on specific categories or subsets of your data, helping you focus onto the relevant insights.
  • Defining Level of Detail: Dimensions determine the level of detail at which your data is analyzed. By adding or removing dimensions from your visualizations, you can change the granularity/method of your analysis, allowing you to zoom in or out on different aspects of your data.


Measures are another facet that is very important for Tableau to function. Here are their main uses.

  • Quantitative Analysis: Measures are used for quantitative analysis of numeric data. They represent the metrics or values you want to analyze, such as sales revenue, profit, or quantity sold.
  • Aggregation: Measures can be aggregated using mathematical functions such as sum, average, count, min, or max. Aggregating measures allows you to summarize and analyze your data at different levels of granularity, from individual data points to overall trends.
  • Calculation and Comparison: Measures can be used to perform calculations and comparisons. You can create calculated fields to derive new metrics or KPIs based on existing measures, allowing you to perform custom analyses and gain deeper insights into your data.
  • Visualization: Measures are essential for visualizing numeric data. They are plotted on quantitative axes in visualizations such as bar charts, line charts, scatter plots, and more, from which you can visually explore and interpret your data.

Important Things To Note

  • Utilize dimensions to categorize and group your data effectively.
  • Use measures for quantitative analysis of numeric data.
  • Create calculated fields to derive custom metrics or KPIs based on existing dimensions and measures.
  • Ensure that you use appropriate aggregations for your measures based on the analysis you want to perform.
  • Avoid using dimensions as measures or vice versa.
  • Avoid overcrowding your visualizations with too many dimensions or measures.
  • Ensure that you format measures appropriately to make them more readable and understandable.

Frequently Asked Questions (FAQs)

1. Can dimensions contain numerical data?

Dimensions can contain numerical characters (such as Customer ID), but they do not possess numerical values. Only measures have numerical data with values.

2. Can measures contain non-numeric data?

It is possible for measures to have non-numeric data, but it is considered as suboptimal to represent non-numeric data as measures. 

3. Can I use parameters with dimensions and measures in Tableau?

Yes. You can create parameters for Tableau dimensions vs measures. For example, a parameter “YEAR” can be categorized from the date given.

4. What is the difference between discrete and continuous dimensions/measures in Tableau?

In Tableau dimensions vs measures, discrete dimensions/measures result in distinct categories, while continuous dimensions/measures represent a continuous range of values. Discrete dimensions are typically categorical, while continuous dimensions are numerical.

This has been a guide to Tableau dimensions vs Measures. Here we learn the conversion of Dimensions & measures in Tableau with key differences, uses, and examples. You can learn more from the following articles –

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