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Table of contents
Tableau Filters
What is Dimension Filter?
Features of Dimension Filter
Implementation of Dimension Filter
Frequently Asked Questions
What is a Dimension filter?
What is Tableau?
What do you mean by aggregated filters?
What are the different types of filters in tableau?
Last Updated: Mar 27, 2024

Introduction to Dimension Filter

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While making decisions based on data, finding useful information from big datasets is very important. Here comes the Dimension filter to our rescue. Dimension Filters are a powerful tool that makes data analysis easier. 

Introduction to Dimension Filter

In this article, we will understand the basics of Dimension Filter and its important features, followed by its implementation and some of its applications.

Tableau Filters

In Tableau, filters limit the data displayed in a visualization or a map based on certain conditions or criteria. These filters reduce the size of large databases, allowing users to focus on specific subsets of data and identify patterns in large datasets. Sometimes the filters are also used to remove unnecessary dimension attributes.

Dimension filter is a powerful data filtration tool offered by Tableau. Let us dive deeper into the topic and understand Dimension Filters in detail.

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What is Dimension Filter?

Dimension filters are an important feature used in data analysis to select and filter data based on particular dimensions or attributes. The dimensions represent the characteristics of the data in the datasets. Each dimension has unique values that can be filtered using the dimension filter. Thus, in contrast to aggregated filters, work on individual attributes or categories of a dataset.

dimension filter

Dimension filters allow users to add and remove values in a dimension. Dimension filters allow analysts to analyze the database on certain fixed dimensions. Thus, identifying valuable data in huge datasets becomes easier and more targeted.

Features of Dimension Filter

Dimension filters have many features that help refine the data analysis process. Some of them are:

  • Inclusion/Exclusion: Dimension filters are used to include and exclude given values from a dimension.
  • Grouping: Dimension filters are also used to create groups within a dimension. This combines related values together, thus making data analysis easier.
  • Sets: We can also create sets with the help of dimension filters. These sets act as custom subsets of data based on particular conditions within a dimension.
  • Binning: The binning feature of dimension filters can be used to group values into predefined ranges. This makes the analysis of continuous or numerical data simple.
  • Top/Bottom conditions: The dimension filter allows users to filter data using the top or bottom condition. For example, selecting the top five employees or the bottom ten performers. 
  • Wildcard Matching: Wildcard characters can be used to search for matching patterns in dimension values. This, in turn, makes the filtering of data easier.
  • Formulas: Formulas can be used in dimension filters to work on custom calculations. These can later be applied as filters to the dimension.

Implementation of Dimension Filter

The steps for the implementation of Dimension filter in your database is as follows:

Step 1:

Identify and select the dimension in your database to which you would wish to apply the filter from the drop-down menu.

Step 2:

Next, you must choose the filter method you want to apply to the dimension. The various methods include inclusion/exclusion of specific members, groupings, sets, bins, top/bottom conditions, wildcard matching, etc.

Step 3:

Depending on your data analysis tool, you have to locate and access the dimension filter functionality.

Step 4:

In this step we define the criteria for the filter based on the chosen method.

Step 5:

Now, apply the filter to the dimension within your dataset.

We have taken a database from our local memory in the example below. The “Orders ID (Returns)” attribute is chosen from the dimensions list. 

Implementation of Dimension Filter

After this, we can select the method as per the requirement and proceed with the further steps.

Implementation of Dimension Filter

Frequently Asked Questions

What is a Dimension filter?

Dimension filters are an important feature used in data analysis to select and filter data based on particular dimensions or attributes. They allow users to add and remove values in a dimension. Dimension filters allow analysts to analyze the database on certain fixed dimensions, thus making their work easier.

What is Tableau?

Tableau is an efficient data visualization tool that is widely used by businesses and analysts. It allows users to connect different databases and Excel sheets. Tableau offers different types of maps to represent data and spot patterns in them.

What do you mean by aggregated filters?

Aggregate filters are a type of filter that operates on combined data. These filters apply conditions to aggregated calculations derived from data. They are useful in analyzing data at a higher level of aggregation and identifying the patterns in big datasets.

What are the different types of filters in tableau?

Tableau offers different types of filters for better filtering of data in data analysis. They are dimension filters, measure filters, extract filters, data source filters, context filters, and user filters.


Kudos, Ninja, on making it to the finish line of this article! This blog has covered all the important fundamentals of Dimension filters and how it has improved decision-making in data analytics.

We hope this blog has helped you understand the implementation of Dimension Filters better. Keep learning! We suggest you read some of our other articles on Data Analytics: 

  1. Introduction to data analysis
  2. Analytics and Big Data
  3. Big Data Analytics Example
  4. State space search in artificial intelligence
  5. Agents in Artificial Intelligence

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