Building a Tableau Treemap 101: Easy Steps, Usage & Benefits

Tableau Treemap is a basic chart type that uses nested rectangular boxes to represent data. This graph can be used to visualize large datasets. Treemap is a graph that can be used to compare hierarchical data. A Tableau Treemap is a useful chart for analyzing data anomalies. Treemaps are simple Data Visualization that can present information in a visually appealing manner.

This article talks about Tableau Treemaps, how they are built and read, and the key benefits of using Treemaps in Tableau.

What is Tableau?

Tableau is a well-known Business Intelligence and Data Analytics tool that was developed to assist in visualizing, analyzing, and understanding complex business data to make data-driven decisions. It is a smart platform that allows businesses to move more quickly and in a way that clients and consumers can understand. The most important feature of this tool is that it makes it extremely simple for users to organize, manage, visualize, and understand data.

Tableau can assist anyone in seeing and comprehending their data. You can connect to any database, create visualizations by dragging and dropping, and share them with a single click. The main objective of Tableau is to help people visualize and understand their data. 

Tableau’s Self-Service Analytics platform enables anyone to work with data, regardless of their skill level. It was aimed to help users create visuals and graphics without requiring the assistance of a programmer or any prior programming knowledge. It is a highly scalable and easily deployable platform.

Key Features of Tableau

• Data Sources: Tableau has plenty of data source options from which you can connect and fetch data. Tableau supports a wide range of data sources, including On-premise files, spreadsheets, relational databases, non-relational databases, Data Warehouses, Big Data, and On-cloud data. Tableau can connect to any of the data sources securely. You can also merge data from multiple sources to create a visual combinatorial view of data. Tableau also works with a variety of data connectors, including Presto, MemSQL, Google Analytics, Google Sheets, and others.
• Advanced Visualizations: Tableau has a wide range of visualizations, including basic visualizations like a Bar Chart and a Pie Chart, as well as advanced visualizations like a histogram, a Gantt Chart, a Bullet Chart, a Motion Chart, a Treemap, and a boxplot. Any kind of visualization can be selected easily under the visualization type from the Show Me tab.
• Robust Security: Tableau takes all precautions to protect data and offers robust user security. For data connections and user access, its security system relies on authentication and permission systems. It employs row-level filtering, which aids in the security of the data. It also allows you to connect to other security protocols like Active Directory, Kerberos, and so on.
• Mobile View: Tableau also provides the mobile version of the software. You can create dashboards and reports that are compatible with your mobile. It also allows you to create customized mobile dashboard layouts that are specific to your mobile device. This feature provides users with a great deal of flexibility and convenience when it comes to managing their data.
• Cross-Database Join: Tableau 10 introduced Cross-Database Join, a new feature that allows you to cross data between different sources much more quickly and without requiring any additional technical knowledge. A Cross-Database Join combines data from two different databases as if they were one. Data sources that join data from multiple databases are created and published so that other Tableau users can create reports.
• Live and In-Memory Data: Tableau ensures that both live data sources and data extraction from external data sources are connected as in-memory data. This allows the user to use data from multiple types of data sources without restriction. You can use data directly from the data source by setting up live data connections or keeping that data in memory. 

What is Tableau Treemap?

The most widely used interactive Data Visualization tool is Tableau. It has a large number of charts to help you easily and effectively explore your data. Data is represented by Rectangle Boxes in Tableau Treemap. Each rectangle box can be determined by any of your Dimension members, and the box size can be determined by the Measure value. Tableau Treemaps are extremely useful for displaying the most complex data set information in a small data region.

The Tableau Treemap was designed to display hierarchical data, but it is now also used to display part-to-whole relationships. Dimensions are used to define the Tableau Treemap’s structure, while Measures are used to define the size and color of the individual rectangles. Treemaps are simple Data Visualization that can present information in a visually appealing manner. When you need to show cumulative totals for the working data, the Tableau Treemap chart is the way to go. When making the chart, you can include labels such as date, time, name, and budget.

A Tableau Treemap’s fundamental components are:

Mark type:	Automatic or Square
Color:	Dimension or Measure
Size	Measure
Label or Detail:	Dimension(s)

When there are many components in a whole, a Tableau Treemap is used to show how they fit together. A rectangular area divided into smaller rectangles to represent sub-categories is typically (but not always) used for the arrangement. The size of these subcategory rectangles is a numerical value. The small rectangles are usually logically grouped into a different categorical family and colored to indicate a key data attribute.

The Tableau Treemap has some drawbacks, such as the fact that it offers very limited customization options to the user and is inefficient at representing data ranges. It’s a good idea to give the Treemap in Tableau proper labels, colors, sizes, and naming conventions so that the visualizations are more meaningful.

Key Benefits of Tableau Treemap

• Scalability: Tableau Treemap performs admirably when dealing with large amounts of data. As the amount of data grows, so does the difficulty in comprehending it. Treemap Charts do not have this issue because the related labels are linked to the data they represent.
• Displays Hierarchical Data: This is the purpose for which it was created. Two layers are usually the best visualization for displaying hierarchical data. The hierarchical data representation is based on a tree-like structure.
• Focus on Highlighting Major Contributors: The major contributors are highlighted in the Treemap by larger rectangles or a bold color. The Tableau Treemap is an appropriate solution if your application has this requirement. When you need to communicate and consume a large number of marks in a single view, Treemap Charts are a great option. A user can easily spot patterns and relationships between them if they follow this crucial behavior.
• Higher Utilization of Space: Nearly the entire area is covered in packed rectangles.

Use Cases Of Tableau Treemaps

When it comes to knowing how to use a tool, practice, or technique, it’s just as important to know how to use it. Because every chart has a working range, consider where a Treemap Chart holds significance and how it is used.

• Only when you need to work with two numerical values.
• Only when you have a lot of hierarchical data and a lot of room to decide.
• If you need a quick and high-level summary of the anomalies and similarities in one or more categories of working data, this is the tool to use.

How to Build a Tableau Treemap?

Here you’ll be making a Treemap Chart showing no profit and no sales in specific years, along with the profit ratios that go with them.

• Step 1: You need to connect the TABLEAU application to a dataset as users:

The ‘Superstore‘ dataset, which comes preinstalled with the application, can be seen in the main section (black area). To create a Treemap Chart, you will use this dataset.

• Step 2: When you click on the Sample Superstore dataset, you’ll see the screen below:

The content of the data set is listed in multiple tabs at the bottom of the screen, indicating that Sample Superstore is fully loaded. This is a type of working data reference chart.

• Step 3: Now you’ll create your own Treemap Chart; when you create a new worksheet on the dashboard, you’ll see something like this:

Only two-dimensional data is considered within a Treemap Chart. On the left-hand side of the screen, you can see the ‘Dimension,‘ ‘Measures,’ and ‘Parameters’ sections. All of these contain various types of data or data measurement techniques. If you look at the screen below, you’ll notice that a Treemap chart requires at least one dimension and two measures.

• Step 4: Then, from the Dimension and Measure section, you drag and drop data. The actual chart-making process starts from here. You want the YEAR and REGIONAL information to be at the top of your chart. So you select ‘Text’ and drag the Region and Year of Order dates to it. You’ll see the screen below once you’ve done so:

• Step 5: Drag and drop ‘Sales’ into the ‘Size’ section to add the Sales information to the chart (present under the Marks) as shown below:

• Step 6: With this, you’ll be able to add the term ‘Profit’ to the chart, which will display the profits earned as a percentage of total sales in a given region over time.

• Step 7: This is the basic structure of a Treemap Chart, but you want to sort the profit by color. Drag and drop the ‘Profit’ mark to the ‘Color’ marks section to accomplish this. Examine the image below:

• Step 8: The information in the Marks section specifies the types of details that will be reflected. Take a look at the illustration below:

This is how you create a Treemap Chart in Tableau; customizations can be made as needed, but the overall process is the same: drag and drops the working data.

How to Read a Tableau Treemap?

Treemaps make it simple for viewers to understand their data at a glance. Color can be used to represent dimensions (like categories) or measurements (such as KPIs). If a darker color is used to represent a KPI, it may draw attention to extremes, such as high or low values. A user could use a categorical palette for dimensions, assigning a different color to each available shipping mode. A Continuous Color Palette would show a company’s sales numbers or profit as a measure.

When looking for insights in a Treemap, the largest box represents the largest portion of the whole, while the smallest box represents the smallest portion. These boxes can be nested to show multiple categories for a more in-depth analysis. “Percentage spent on furniture,” for example, might be displayed in a box within the “office expenses” data set. “Percentage spent on desks” might be displayed in a box nested within that box.

Look for significant color variations and the largest rectangular values (or the largest collected group of rectangles). As the rectangles get smaller, effective labeling becomes more difficult, so only the most prominent values are often labeled. Treemaps are interactive visualizations that allow you to click or hover over different shapes to reveal more information about the category.

When visualizing a large number of related categories, Treemaps come in handy. A bar chart may be a better choice if specific values need to be displayed prominently.

Limitations Of Tableau Treemap

The Tableau Treemap has some drawbacks and limitations, they are: 

• Difficult to Make Accurate Comparisons: People are better at comparing the length or position of rectangles than the area. Another issue is that, in most cases, the components you want to compare lack a common baseline. Comparisons can be made more quantitatively and accurately with bar charts, dot plots, and line charts.
• No Labels on Small Components: A Treemap can display many categories, but if there are too many components, the rectangles can become very small. Tableau is unable to display all labels. As a result, you’ll have to rely on Tableau’s interactive features, such as tooltips and highlights.
• Few Customization Options: Data with varying ranges are not displayed correctly in the Treemap Chart. Users are not given any sorting options for the chart. Because it contains a large number of data points, it is not suitable for printing. 

Conclusion

This article talks about one of the standard charts, Tableau Treemap. It covers the basics of Treemap creation as well as the concept and features of Treemaps.

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