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Building and Customizing Hierarchies in Tableau

Building and customizing hierarchies in Tableau is a crucial skill for data analysts and business intelligence professionals. Hierarchies allow users to organize and analyze data in a structured manner, enabling them to gain valuable insights and make informed decisions. In this article, we will explore the concept of hierarchies in Tableau, discuss their importance, and provide step-by-step instructions on how to build and customize hierarchies in Tableau. We will also delve into advanced techniques and best practices to help you optimize your hierarchical analysis. So, let’s dive in!

Understanding Hierarchies in Tableau

Before we delve into the process of building and customizing hierarchies in Tableau, it is essential to have a clear understanding of what hierarchies are and how they function within the software.

In Tableau, a hierarchy is a logical arrangement of fields that represent different levels of detail within a dataset. It allows users to drill down or roll up data to view it at different levels of granularity. For example, a hierarchy for a sales dataset could include fields such as Region, Country, and City, where Region is the highest level of detail, followed by Country, and then City.

Hierarchies in Tableau are represented by a tree-like structure, with the highest level of detail at the top and the lowest level at the bottom. Users can navigate through the hierarchy by expanding or collapsing levels to focus on specific levels of detail.

Now that we have a basic understanding of hierarchies in Tableau, let’s explore the process of building and customizing them.

Building Hierarchies in Tableau

Building hierarchies in Tableau is a straightforward process that involves selecting the relevant fields and arranging them in the desired order. Here’s a step-by-step guide on how to build hierarchies in Tableau:

  1. Open Tableau and connect to your dataset.
  2. Drag and drop the fields you want to include in the hierarchy onto the Rows or Columns shelf.
  3. Arrange the fields in the desired order by dragging and dropping them within the shelf.
  4. Right-click on the top-level field and select “Create Hierarchy” from the context menu.
  5. Repeat the previous step for each subsequent level in the hierarchy.

Once you have built the hierarchy, you can use it to analyze your data at different levels of detail. Tableau provides various tools and features to interact with hierarchies, such as drill-down, roll-up, and filtering.

Customizing Hierarchies in Tableau

While Tableau automatically creates hierarchies based on the order of fields, you can customize them to suit your specific analysis requirements. Customizing hierarchies allows you to control the levels of detail and the order in which they appear. Here are some techniques to customize hierarchies in Tableau:

Reordering Levels

To change the order of levels within a hierarchy, you can simply drag and drop the fields within the Rows or Columns shelf. For example, if you want to change the order of the Region, Country, and City levels in a sales hierarchy, you can drag the City field above the Country field to make it the second level.

Adding or Removing Levels

In some cases, you may want to add or remove levels from a hierarchy. To add a level, you can drag and drop a field onto the Rows or Columns shelf in the desired position. To remove a level, you can right-click on the field and select “Remove” from the context menu.

Creating Calculated Fields

Tableau allows you to create calculated fields based on existing fields in your dataset. You can use calculated fields to add additional levels to a hierarchy or modify the existing levels. For example, you can create a calculated field that combines the Region and Country fields to create a new level called “Region-Country.”

Using Sets and Groups

Sets and groups are powerful features in Tableau that allow you to define custom subsets of data. You can use sets and groups to create new levels within a hierarchy or modify the existing levels. For example, you can create a set that includes only the top-performing cities within a country and add it as a new level in the hierarchy.

Advanced Techniques for Hierarchical Analysis

Now that we have covered the basics of building and customizing hierarchies in Tableau, let’s explore some advanced techniques that can enhance your hierarchical analysis:

Drill-Down and Roll-Up

Tableau provides drill-down and roll-up functionality that allows users to navigate through hierarchies and view data at different levels of detail. You can use the drill-down feature to expand a hierarchy and view more detailed information, while the roll-up feature allows you to collapse the hierarchy and view higher-level summaries. These features are particularly useful when analyzing large datasets with multiple levels of detail.

Filtering Hierarchies

Tableau allows users to filter hierarchies based on specific criteria. You can apply filters to individual levels within a hierarchy or filter the entire hierarchy as a whole. Filtering hierarchies can help you focus on specific subsets of data and gain deeper insights into your analysis.

Creating Hierarchical Calculations

Tableau provides a powerful calculation engine that allows users to create complex calculations based on hierarchical data. You can use hierarchical calculations to perform aggregations at different levels of detail, calculate ratios and percentages, and perform other advanced analysis. Hierarchical calculations can be created using Tableau’s calculation editor or by writing custom formulas using Tableau’s calculation language.

Using Table Calculations

Table calculations are another advanced feature in Tableau that can be used to perform calculations on hierarchical data. Table calculations allow users to perform calculations across different levels of detail within a hierarchy, such as calculating running totals, percent of total, and moving averages. Table calculations can be added to any visualization in Tableau and can be customized to suit your specific analysis requirements.

Best Practices for Hierarchical Analysis in Tableau

To ensure the accuracy and effectiveness of your hierarchical analysis in Tableau, it is important to follow some best practices. Here are some tips to help you optimize your hierarchical analysis:

  • Ensure data consistency: Before building hierarchies, make sure that the data is clean, consistent, and properly formatted. Inconsistent or missing data can lead to inaccurate analysis results.
  • Choose the right granularity: Select the appropriate levels of detail for your hierarchies based on the analysis requirements. Too many levels can make the analysis complex and overwhelming, while too few levels may not provide enough insights.
  • Use meaningful labels: Assign clear and descriptive labels to the levels within your hierarchies. Meaningful labels make it easier for users to understand the data and navigate through the hierarchy.
  • Optimize performance: Hierarchical analysis can be resource-intensive, especially when dealing with large datasets. To optimize performance, consider aggregating data at higher levels of detail or using data extracts instead of live connections.
  • Test and iterate: Experiment with different hierarchies and analysis techniques to find the most effective approach for your specific analysis requirements. Test your hierarchies with sample data before applying them to the entire dataset.


Building and customizing hierarchies in Tableau is a fundamental skill that can greatly enhance your data analysis capabilities. Hierarchies allow users to organize and analyze data at different levels of detail, providing valuable insights and facilitating informed decision-making. By following the step-by-step instructions and best practices outlined in this article, you can effectively build and customize hierarchies in Tableau and optimize your hierarchical analysis. So, start exploring the power of hierarchies in Tableau and unlock the full potential of your data!

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