Understanding Tableau Data Types
Updated: May 30, 2025
When it comes to creating insightful and interactive dashboards in Tableau, understanding data types is crucial. Think of data types as the foundation of your data analysis—they determine how Tableau reads, interprets, and visualizes your data. Misunderstanding or misassigning data types can lead to misleading visualizations or calculation errors.
In this blog, we’ll explore the different data types in Tableau, how they work, and why they matter.
Every field (or column) in your dataset has a data type, which tells Tableau what kind of data it is working with.
Tableau:
Tableau is a powerful and widely used data visualization and business intelligence (BI) tool that helps people see and understand their data. It allows users to connect to various data sources, analyze data, and create interactive, shareable dashboards and reports.
Below image shows some sample Online Store Sales DATA
Tableau Data Types
Order ID | Customer | Category | Sales ($) | Profit ($) | Region |
101 | Alice | Electronics | 1,200 | 300 | East |
102 | Bob | Furniture | 250 | 50 | West |
103 | Carol | Office Supplies | 75 | 20 | South |
104 | Dan | Electronics | 800 | 180 | North |
105 | Emma | Furniture | 150 | 30 | East |
There are 7 main data types in Tableau
1. Numeric Data Type:
This data type contains numbers like integers or decimal values.
Example in below image OrderID,Sales column and Profit column has Number Values.
Order ID | Sales ($) | Profit ($) |
101 | 1200 | 300 |
102 | 250 | 50 |
103 | 75 | 20 |
104 | 800 | 180 |
105 | 150 | 30 |
Usage
Numbers are commonly used as Measures in Tableau, where they can be aggregated (summed, averaged, etc.).
Numeric fields are often placed on the Axes of visualizations like bar charts, line graphs, or scatter plots.
You can perform arithmetic operations like addition, subtraction, or complex statistical calculations using numeric fields as Calculative Field
2. String Data Type
This data type contains text value. e.g. Name, Labels etc. String can also include digit or symbols.
Example in below image Customer,Category and Region column has string values.
Customer | Category | Region |
Alice | Electronics | East |
Bob | Furniture | West |
Carol | Office Supplies | South |
Dan | Electronics | North |
Emma | Furniture | East |
Usage
Strings are typically used as Dimensions, meaning they categorize the data. For example, provider names or visit types can be dimensions in your visualizations.
You can Filter visualizations based on string fields, such as filtering by specific types or names.
3. Date Data Type
This data type contains date value for any formate 'DD-MM-YYYY' or 'MM-DD-YYYY'. The date value represents specific time period, which is used for analysis over time, means monitoring changes in days, months or years.
Example in below image, Order Date column has date values
Here is an example showing how the Date Data Type can be used in tabular format:
Order Date | Customer | Category | Region |
15-05-2025 | Alice | Electronics | East |
20-05-2025 | Bob | Furniture | West |
22-05-2025 | Carol | Office Supplies | South |
25-05-2025 | Dan | Electronics | North |
27-05-2025 | Emma | Furniture | East |
Order Date |
15-05-2025 |
20-05-2025 |
22-05-2025 |
25-05-2025 |
27-05-2025 |
Date and Time Data Type
An extension of date data type is date and time Data type. A date and time value can be continuous or discrete. It supports all formate of time. There is no time data type in tableau, so comparison will still look at the date component.
Order Date & Time |
15-05-2025 09:30 AM |
20-05-2025 02:15 PM |
22-05-2025 11:45 AM |
25-05-2025 04:00 PM |
27-05-2025 08:20 AM |
Uses
Time Series Analysis: Dates are commonly used for time-based analyses, such as tracking sales over time, plotting trends, or analyzing seasonal patterns.
Tableau can automatically generate Date Hierarchies (Year, Quarter, Month, Day) to allow you to drill down or roll up across time periods in your analysis.
You can create date Filters for specific ranges (e.g., last month, last 30 days, etc.).
Calculated Functions like DATEDIFF(), DATEADD(), and DATEPART() allow you to manipulate date fields, calculate differences between dates, or extract specific parts (e.g., year, month).
4. Geographical Data Type
This data type contains geographical field values such as 'Country name', 'Region','City name','Zip code' and 'territories'. Useful to see Geographical map view in tableau.
Example in below image, City column and State column has geographical data values
City | State |
New York | New York |
Los Angeles | California |
Chicago | Illinois |
Houston | Texas |
Miami | Florida |
Usage
Geographic fields allow you to plot data on Maps. For example, if you have country or city names, Tableau can visualize this on a world map or regional map.
Tableau has built-in Geocoding to automatically recognize geographic names, but you can also manually assign custom latitude and longitude data if needed.
5. Boolean Data Type
This data type contains numeric values like 'True' or 'False'. Boolean data type is binary data type which has only two type of values '0' or '1'. This data type is useful for categorized data in two distinct outcome.
Customer | Purchase Made |
Alice | True |
Bob | False |
Carol | True |
Dan | True |
Emma | False |
Usage
Boolean fields are useful for creating Filters that depend on conditions, such as showing only rows where a condition is TRUE.
You can use Boolean fields to control the display of visualizations based on certain conditions (e.g., highlighting rows where profits are negative) is Conditional Formating
6. Cluster Data Type
This data type contains mixed value
Cluster ID | Customers | Avg Sales ($) | Region | High Profit (True/False) |
1 | Alice, Dan | 1,000 | East, North | True |
2 | Bob, Emma | 200 | West, East | False |
3 | Carol | 75 | South | True |
7. Null Values
Null values represent missing or undefined data. Tableau highlights these null values so you can either account for or filter them out of your analysis.
Usage
Tableau allows you to manage null values in visualizations. You can choose to filter them out, display them separately, or replace them with a default value.
Use Null functions like ISNULL() to handle null values in calculations.
Conclusion
For individuals beginning their journey with Tableau, a fundamental understanding of its data types is crucial. Selecting the appropriate data type ensures the accuracy of analyses, minimizes errors, and facilitates efficient adjustments within Tableau.
Overall, Tableau’s data types are intuitive and accessible for most users. Therefore, it can be confidently stated that Tableau data types are easy to comprehend, particularly when one starts with the foundational concepts.
