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Table of contents
1.
Introduction
2.
What is Tableau?
3.
What are Dimensions and Measures in Tableau?
3.1.
Dimensions in Tableau
3.2.
Measures in Tableau
3.3.
Uses of Dimensions
3.4.
Uses of Measures
4.
Examples of Dimensions and Measures in Tableau
4.1.
Examples of Dimensions
4.2.
Examples of Measures
5.
Converting Dimension to Measure
6.
Converting Measure to Dimension
7.
Frequently Asked Questions
7.1.
What is a Tableau?
7.2.
What are the Additive Measures in Tableau?
7.3.
What are the uses of Geographical Region Dimension in Tableau?
8.
Conclusion
Last Updated: Mar 27, 2024
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Dimensions and Measures in Tableau

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Introduction

Hello Ninja, I hope you are doing great. Do you know Dimensions and Measures in Tableau? If not, don't worry. We are here to enrich your knowledge and clear all your doubts.
Dimensions and Measures are the two essential aspects for organizing, summarizing, and visualizing the information effectively in Tableau. We will cover all the details related to these two aspects in detail.
 

Dimensions and Measures in Tableau


This article will discuss the topic of Dimensions and Measures in Tableau. We will thoroughly discuss the uses of Dimensions and Measures in Tableau and their respective examples.
We will also cover converting a Dimension to a Measure and vice versa.

What is Tableau?

Tableau is a data visualization and business intelligence tool widely used by Data Analysts and professionals from various fields. It excels in quickly processing input data and providing meaningful visualization outputs. Tableau can connect to multiple data sources, including databases and cloud services, which allow us to combine data from numerous sources for detailed analysis. It offers a wide range of visualizations, including charts, tables, and maps, to represent the data meaningfully.

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What are Dimensions and Measures in Tableau?

Dimensions in Tableau

Dimensions are the fields that slice and describe data records containing a primary key and textual information. These data fields contain qualitative information, such as texts and discrete values. The Dimensions provide descriptive attributes to our data, allowing us to categorize and group it for visualization purposes.

Dimensions in Tableau are divided into different categories.

Types of Dimensions Description
Slowly Changing Dimensions (SCDs) If the data in the dimensions change over time, then such kinds of Dimensions are called SCDs. The data warehouse processes typically manage these.
Rapidly Changing Dimensions (RCDs) These Dimensions involve attributes that undergo frequent modifications or fluctuations such as rapidly changing customer preferences.
Unchanged Dimensions If data in the dimensions are constant or won’t be changed throughout the visualization process, it is called Unchanged Dimensions or Static Dimensions. 
Conformed Dimensions These are the Dimensions shared by the multiple business areas and they provide a common reference to analyze the data from the different sources.
Shrinking Dimensions  These are the subsets of the Dimensions that we get by eliminating the unnecessary rows or columns of the dimension tables.
Transactional Dimensions These are the Dimensions in which all the values get stored in the fact tables instead of dimension tables. 
Junk Dimensions These are the Dimensions that store the junk data. These Dimensions are the tables with a combination of relevant or irrelevant data such as flags and indicators used to avoid a large number of foreign keys in the fact table.

Measures in Tableau

Measures are the fields that contain quantitative or numerical information that we analyze or perform calculations on. These are displayed as the discrete or continuous values which serves as the foundation for performing complex mathematical operations.

Based on the level at which it can be aggregated, Measures are divided into three categories.

Types of Measures Description
Additive Measures These are the Measures that can be summed or aggregated across all Dimensions of the visulalization process. It supports all the group functions such as min, max, and avg.
Semi-Additive Measures These are the Measures that can be aggregated across some of the Dimensions and may vary based on the Dimension being used. It supports only a few of the group functions.
Non-Additive Measures These are the measures where we can’t apply any type of aggregations. We cannot use group functions in measures like statistical calculations or percentages.

Uses of Dimensions and Measures in Tableau

Uses of Dimensions

  • Dimensions allow us to group and organize the data set into different categories to analyze it effectively and compare it across different dimensions.
     
  • They define the axis of the graph and provide labels to the charts, which helps us to interpret the data easily.
     
  • Dimensions can be used to create a hierarchical structure that helps us analyze the data at different levels. 
     
  • Dimensions filter out the data based on specific categories and prioritize the categories in the visualization process.

Uses of Measures

  • Measures allow us to apply aggregate operations such as min, max, and count to summarise the numerical values.
     
  • Tableau provides a calculation engine to create our custom formulas using the Measures. This allows us to perform complex analytics within Tableau.
     
  • Measures are used to create references for our data, allowing us to compare the data set against predefined targets.
     
  • Measures offer various statistical functions used to perform statistical analysis and provide insights into the data relationships.

Examples of Dimensions and Measures in Tableau

Examples of Dimensions

Examples of Dimensions


The above image shows most examples of Dimensions in Tableau, such as product category, geographical region, order status, and many more.

Let's discuss some of these examples one by one.

  • Customer Name: A Dimension in Tableau analyzes the data based on individual customers. Using this Dimension, we can analyze the total sales amount for each customer, the number of repeat purchases made by each customer, and visualize the sales distribution among different customers.
     
  • Location: This Dimension allows us to analyze the data based on geographical information. Using this Dimension, we can identify the areas with low or high sales, create bar charts to identify the top-performing regions and focus on a particular subset of our geographical data.
     
  • Product: It represents the different products in our dataset. It visualizes the data based on the various products and their performances. We can identify the top-selling products and potential growth areas using this Dimension. We can also create line charts to track the sale trends and identify the seasonal patterns for each product.
     
  • Order Date: This Dimension allows us to visualize the data based on temporal information. This helps us identify seasonal patterns or trends and compares sales performance across different periods.

Examples of Measures

Examples of Measures


The image above shows examples of Measures used to perform calculations and aggregations.

Let's discuss some of these examples one by one.

  • Discount: It is a measure that represents the amount of discount applied to a particular sale. It analyzes the impact of discounts on sales and compares the profit between old and new sales. It helps to identify which categories have higher sales and check whether higher discounts lead to increased sales.
     
  • Profit: It represents the gain or loss obtained from sales. It calculates the overall profitability of the sales and visualizes the total profit over a specific period. It enables us to identify the growth opportunities and the regions that generate higher revenue. 
     
  • Count of Orders: It represents our dataset's total number of orders. It calculates the average order frequency by dividing the total number of orders by the number of unique customers, which helps to understand how frequently customers place orders.
     
  • Count of Returns: It provides information about the volume and frequency of the returned items. It helps us to analyze the quality of products and customer care services. By plotting the Count of Returns with the Return Reason Dimension, we can identify the common reasons behind the returned items and take appropriate actions for them.

Converting Dimension to Measure

Converting a Dimension to Measure


We can convert a Dimension to a Measure by aggregating and summarizing the Dimension based on a specific calculation.
If our Dimension is not associated with any numerical values, we can convert it into a Measure by counting the occurrences of each category. To do this, we use a Measure option, 'Count.'
If our Dimension is associated with the numerical values, we can convert it into a Measure by summarizing them. We use aggregate functions such as 'sum,' 'min,' 'avg,' and 'max.'
We have converted a Dimension 'Customer Name' into a Measure in the image above. It uses a default aggregate function, 'Count,' and assigns a numerical value to each customer name occurrence.

Converting Measure to Dimension

Converting a Measure to Dimension


We convert a Measure to Dimension to analyze the numerical values as the qualitative variables. By converting a Measure to a Dimension, we can gain some insights into the patterns and trends from the numerical data. It allows us to explore the data at different granular levels providing detailed analysis and understanding of the factors contributing to Measures.
In the image above, we have converted a ‘Customer Name Count’ Measure to a ‘Customer Name’ Dimension for grouping, filtering, or performing further analysis based on the Customer Names. 

Also Read, Descriptive Statistics

Frequently Asked Questions

What is a Tableau?

Tableau is a data visualization software that provides tools and features to transform raw data into graphical data. We can create graphs, maps, charts, and other visual data representations with Tableau.

What are the Additive Measures in Tableau?

The Additive Measures in Tableau are the Measures that can be summed or aggregated across all the Dimensions of the visualization process. They support various group functions including ‘min’, ‘max’, ‘avg’, and many more.

What are the uses of Geographical Region Dimension in Tableau?

It identifies the areas with high or low sales and creates line charts or bar charts to identify the top performing regions. It enables us to focus our analysis on a particular subset of the geographical data.

Conclusion

In this blog, we’ve discussed Dimensions and Measures in Tableau. We have covered most of the examples of Dimensions and Measures in Tableau and their respective uses. We’ve also seen the conversion of Dimension to Measure and vice versa.
We hope you enjoyed this article and gained some insights into this topic.
You can refer to Tableau Interview Questions and Tableau and Big Data to know more about Dimensions and Measures in Tableau.
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