Tableau Data Types: Quick Guide and Tutorial

Last Modified: March 27, 2024 - 3 min read

Julian Alvarado

Understanding Tableau data types is foundational for any data analyst or business intelligence professional looking to leverage the full power of Tableau’s visualization capabilities.

Tableau simplifies data analysis by intelligently assigning data types, but it also provides flexibility to adjust these as your dataset requirements evolve.

This blog will teach you what you need to know. Let’s dive in!

Understanding Tableau Data Types

Before we dive into Tableau data types, it’s crucial to discuss the importance of access to accurate data.

Coefficient streamlines this process by enabling the direct import of live data from various business systems into spreadsheets. This ensures your data is accurate.

Here is an overview of the primary data types utilized within Tableau:

  • String: Utilized for text data that does not require mathematical operations. Ideal for descriptive data such as names and categories.
  • Numeric: Includes integers (whole numbers) and floats (numbers with a fractional component), catering to a range of quantitative analyses.
  • Boolean: Represents binary choices within data, encapsulating decisions in a true/false format.
  • Date: Essential for recording specific dates, enabling temporal analysis without time granularity.
  • Date & Time: Expands upon the Date type by incorporating specific times, offering precision in timestamp analysis.
  • Geographic: Facilitates location-based analyses through map-based visualizations.
  • Cluster Group: Represents aggregated groups identified within Tableau’s analytical features, aiding in segmented analysis.

Identifying and Changing Data Types in Tableau

Interacting with Numeric Data Types

Tableau’s intuitive interface simplifies interactions with various data types. For instance, observing automatic aggregation in the ‘Profit’ field underscores Tableau’s recognition of numeric data.

Let’s walk through how it works!

Navigate to the ‘Profit’ field to see automatic aggregation, highlighting its numeric nature.

Viewing automatic aggregation in the 'Profit' field in Tableau, showcasing its numeric data nature.

Click the dropdown next to ‘Profit’ to access data type change options.

Accessing data type change options for the 'Profit' field through the dropdown menu in Tableau.

Explore numeric data type options like decimal and whole numbers.

Exploring numeric data type options, including decimal and whole numbers, in Tableau for data precision.

Identify and click the data type symbol on the left of ‘Profit’ for quick adjustments.

Quickly adjusting the data type of the 'Profit' field in Tableau by clicking the data type symbol.

Exploring Dates and Date Times

Moreover, transitioning between data types, such as converting ‘Shipping Date’ from a simple date to a detailed date and time format, is also straightforward.

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Identify the ‘Shipping Date’ field by its calendar icon, indicating a date data type.

Identifying the 'Shipping Date' field by its calendar icon in Tableau, denoting a date data type.

Convert ‘Shipping Date’ to a date and time format by selecting the ‘Date and Time’ option.

Converting 'Shipping Date' to a date and time format in Tableau for detailed temporal analysis.

“Drag ‘Shipping Date’ into columns and choose ‘Week’ for a continuous axis, then add a product dimension to rows for a temporal analysis.

Dragging 'Shipping Date' to columns, selecting 'Week' for continuous analysis, and adding product dimension in Tableau.

Utilizing String Data Types

Switch focus to string data types, like categories or customer IDs, for identification purposes.


Understand the context of numerical customer IDs as strings for individual identification, not for calculations.

Understanding numerical customer IDs treated as strings in Tableau for individual identification rather than calculations.

Best Practices and Tips

To ensure your visuals are as accurate and insightful as possible, adhere to a few golden rules:

  • Always review Tableau’s auto-assigned data types; while efficient, they’re not infallible.
  • Maintain consistency in data types for fields used in calculations to avoid unfortunate errors.
  • Fields that serve as identifiers warrant being treated as Strings to prevent unintended data treatment.
  • Choice of Date vs. Date & Time should hinge on your analysis’ required detail level.

Conclusion: Leveraging Tableau Data Types for Rich Insights

Understanding Tableau’s data types is more than a technical skill—it’s about ensuring your data tells the story you want. And with Coefficient, simplifying the preliminary data process can level up your Tableau game. See how Coefficient can get you there.

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Julian Alvarado Content Marketing
Julian is a dynamic B2B marketer with 8+ years of experience creating full-funnel marketing journeys, leveraging an analytical background in biological sciences to examine customer needs.
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