Tableau is one of the most popular tools in data visualization and analysis that facilitates brands across all domains to leverage the reckoning potential of acquiring Business Intelligence. For its seamless capability to yield readable insights and simplified dashboards, tableau has been instrumental for even non-technical subscribers to have access to personalized datasheets.

Tableau Filters benefit organizations because they help them present insightful data to clients and business stakeholders. This data is presented in the form of a worksheet or a dashboard. This facilitates better decision-making in business. The tableau filters can filter out sensitive data and share it only with those with access authority.

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There are different types of filters in a tableau that can be used to organize data based on predefined conditions and use them for data visualization. Such ability to filter large data sets in the Business Intelligence tool helps prepare for analysis, including removing irrelevant data records, reducing data sizes for faster processing, and more. The filters are required to highlight any underlying insights that can be derived from the data upon visualizing in a readable, actionable format. Check out our data science courses to learn more about data visualization.

You can go through the different types of Tableau filters discussed below if you are confused about which of the following are appropriate uses for filters in tableau? select all that apply.

What are Filters in Tableau?

Filters in Tableau are essential tools that allow users to refine and control the data displayed in visualizations. Serving as a means of data subset selection, different types of filters in Tableau are primarily used to focus on specific portions of the dataset based on predefined conditions or criteria. These conditions can be applied at different levels, such as the data source, worksheet, or dashboard. 

Filter types in Tableau play a pivotal role in enhancing the analytical process by enabling users to isolate relevant information, exclude unnecessary data, and tailor visualizations to specific subsets. They contribute to creating more meaningful and targeted insights by facilitating dynamic adjustments to the displayed data. 

Whether it’s restricting the timeframe through relative date filters, emphasizing top performers, or refining data based on custom conditions, Tableau filters empower users to interactively explore and analyze datasets, fostering a more efficient and tailored data visualization experience.

Why Do We Perform Filtering in Tableau?

Filtering in Tableau serves several important purposes, contributing to the overall effectiveness of data analysis and visualization. Here are some key reasons why filtering is performed in Tableau-

• Focus on relevant data: Filtering allows users to concentrate on specific subsets of data that are relevant to their analysis. By excluding unnecessary information, users can focus on the key aspects of their dataset, making it easier to identify patterns and insights.
• Enhance data exploration: Filters provide an interactive way to explore and interact with data. Users can dynamically adjust the displayed information, drill down into details, and gain a deeper understanding of the dataset by isolating specific dimensions or measures.
• Improve performance: Applying filters can significantly enhance performance by reducing the amount of data loaded and processed. This is particularly important when dealing with large datasets, as filters help optimize the speed of visualizations and dashboards.
• Tailor visualizations: Filters allow users to customize visualizations based on specific criteria. Whether it’s highlighting top-performing items, focusing on a particular time period, or isolating specific categories, filters help create more targeted and meaningful visualizations.
• Support comparative analysis: Filtering enables users to conduct comparative analysis by selectively including or excluding data points. This is useful for scenarios such as comparing performance across regions, products, or time periods, providing valuable insights into trends and variations.
• Simplify dashboards: Filters play a key role in simplifying dashboards by offering users the ability to control what they see. This helps in creating clean and concise visualizations that communicate insights effectively without overwhelming the audience with unnecessary details.
• Dynamic time analysis: Filters like relative date filters in Tableau allow for dynamic time analysis. Users can easily switch between different time periods, compare trends, and analyze changes over time without manually adjusting date ranges.
• Interactivity for users: Interactive dashboards with filters empower end-users to explore data on their terms. Quick filters and other interactive elements provide a user-friendly experience, allowing individuals to tailor the visualization to their specific needs and questions.

How Many Types of Filters Are There in Tableau?

There are six types of filters in Tableau that users can apply to their data to refine and customize their visualizations. These filter types in Tableau collectively provide users with a powerful set of tools to refine and analyze data in Tableau, offering flexibility and interactivity in exploring insights from their datasets. They are:

• Extract Filters
• Context Filters
• Data Source Filters
• Measure Filters
• Dimension Filters
• Table Filters

Before going into detail about each filter, here’s a brief overview of what each of the 6 filters listed above offer to users. 

Extract Filters enable users to limit the data extracted from the original source, optimizing performance. Context Filters in Tableau help prioritize and limit data by creating subsets that subsequent filters will consider.  Tableau’s Data Source Filters operate at the source level, affecting the entire workbook by restricting the data available for analysis.

Measure Filter in Tableau, on the other hand, enable the filtration of data based on specific measures, offering flexibility in analyzing numerical aspects. The Dimension Filter in Tableau allow users to filter data based on specific dimensions, refining the focus of visualizations.

Lastly, Table Filters provide an interactive way to filter data directly within a table, allowing for a more detailed examination of specific elements. 

Different Types of Filters in Tableau

Filters are a smart way to collate and segregate data based on its dimensions and sets to reduce the overall data frequency for faster processing. There are six different types of filters in tableau desktop based on their various objectives and are mentioned below as per their execution steps.

1. Extract Filters

As understood by its name, the extract filters are used to extract data from the various sources, by saving a screengrab of the way it gets added on your file. Such methods can help in lowering the tableau queries to the data source. As soon as you are done extracting data into your dashboard, you can create the extract and execute Hide All Unused Files to clear the columns unused in the datasheet of your panel. 

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The extract filter in Tableau extracts a tiny subset of data from the primary data source. Subsequently, Tableau creates the data set’s local copy, which will be saved in the repository.

This filter allows you to save a screenshot of how it appears in your workbook. The corresponding methods decrease Tableau queries. The data size can be decreased even more by implementing the dimension filter or measuring the extract as needed.

The steps to create an Extract filter in Tableau:

1. Connect Tableau with the text file.
2. Click on the “Extract button”. It will create a local copy in the Tableau source.

iii. Select the “Edit” option from the drop-down menu close to the Extract button in the upper right corner. It will open the Extract Data window. Now select the “Add” option in the Window.

1. In this step, you need to select a Tableau filter condition from the “Add Filter” window. You can add any of the displayed fields as an Extract filter. Now select the category from the list and then click “OK”.
2. A filtered window will be shown. It depicts data that was extracted through the Extract Tableau filter. You can customize the list or use all values within the list.

The extract filter is one of the versatile tableau filters. This is because it presents various options in addition to the general category to extract data. For example, the Wildcard option helps you to filter fields through a Wildcard match. It allows users to type the character, and the field will be filtered as per the match. The various types of matches are:

1. Contains: Select the members if the member name comprises typed characters.
2. Starts with: Select the members if the member name begins with typed characters.

iii. Ends with: Select the members if the member name terminates with typed characters.

Exactly matches: Select the members if the member name precisely matches with typed characters. 

These matches help you to customize your data and finally provide you with filtered data.

 You can filter data using various Byfield conditions after implementing the below steps:

1. Select the “Condition” tab in the Filter window.
2. Click on the “Byfield” button.

iii. Select the name of the field you want to filter.

1. Now select the aggregation type like average, sum, and median from the drop-down list.
2. Select an operator from the drop-down list.
3. Enter the value to filter the selected field.

vii. Click “OK”.

2. Data Source Filter

Used mainly to restrict sensitive data from the data viewers, the data source filters are similar to the extract filters in minimizing the data feeds for faster processing. 

The data source filter in tableau helps in the direct application of the filter environment to the source data and quickly uploads data that qualifies the scenario into the tableau workbook. To execute such processes, you need to go to the Data Source tab and select the Add option in the upper right corner. 

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Clicking on the Add option in the menu would open into a dialog box, where you can select the field and choose through the values you want to record. Once you press confirmation, you shall be presented with a summary of the presets selected from the data source filters. 

3. Context Filter

A context filter is a discrete filter on its own, creating datasets based on the original datasheet and the presets chosen for compiling the data. Since all the types of filters in tableau get applied to all rows in the datasheet, irrespective of any other filters, the context filter would ensure that it is first to get processed.

Despite being constrained to view all data rows, it can be implemented to choose sheets as and when required to optimize its performance by minimizing the data efficiently. 

The context filter helps in applying a relevant, actionable context to the entire data analysis in tableau. If there are multiple filter preset categories used in the worksheet, dividing it into many parts can overall turn into a context filter in itself that guides all the other filters present in the datasheet. 

It helps you to generate data sets by employing appropriate presets for compilation. It is always processed first, although other tableau filters are used. The multiple preset categories existing in the worksheet can be categorized into several parts that would work like a context filter. The data sets are generated according to the original datasheet. Moreover, the data can be efficiently minimized to allow viewing of all data rows, notwithstanding the constraints. You can choose the sheets when required.

You need to open the Context menu of a prevailing categorical filter and choose “Add to Context”. The context is calculated after the view is created. Subsequently, the context is used to count all other filters.

Go to the context menu of a prevailing categorical filter and select Add to Context to make a Context filter in tableau. Once the view is created, the context is calculated. The context is then used to calculate all other filters. You can use Context Filter to find the topmost 10 subcategories of items in the Furniture category.

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4. Dimension filter

Now that you’ve chosen the data, you can access the values highlighted or remove them from the selected dimension, represented as strikethrough values. You can click All or None to select or deselect based on your operation in case of multiple dimensions. 

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5. Measure Filters

In this filter, you can apply the various operations like Sum, Avg, Median, Standard Deviation, and other aggregate functions. In the next stage, you would be presented with four choices: Range, At least, At most, and Special for your values. Every time you drag the data you want to filter, you can do that in a specific setting. 

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Aggregated filters are always employed after non-aggregated filters, irrespective of the order present on the Filters pane. Measure filters are used to measure fields comprising quantitative data.

Measure filter in tableau shows four types of filters, as mentioned below.

• Range: Selects the range of values to incorporate into the result. This could include specifying a minimum and maximum value to include in the result set. For instance, if you’re analyzing sales data, you might use the Range filter to focus on a specific sales range, such as values between $100,000 and $500,000.
• At least: Selects a measure’s minimum value of. This filter is particularly useful when you want to emphasize a lower limit for a measure. For example, you might use “At Least” to analyze products with sales exceeding a certain threshold.
• At most: Selects a measure’s maximum value. This filter is beneficial when you want to emphasize an upper limit for a measure. For instance, you might use “At Most” to focus on customers with a purchase history below a certain total.
• Special: Selects null or non-null values. This can be useful when dealing with missing data or when you specifically want to analyze data points with null values. For instance, you might use the Special filter to isolate and investigate cases where certain measures are not available.

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6. Table Filters

The last filter to process is the table calculation that gets executed once the data view has been rendered. With this filter, you can quickly look into the data without any filtering of the hidden data. 

Unlike other types of filters in Tableau applied during the data processing phase, the table calculation filter operates post-rendering, allowing users to interact with the data without directly affecting the underlying dataset.

When you apply a table calculation filter, you essentially perform calculations on the displayed results rather than the raw data itself. This allows for dynamic and interactive exploration of the data without applying permanent changes to the dataset. Users can quickly analyze trends, patterns, or outliers without altering the original data view or applying restrictions to hidden data.

This type of filter is particularly valuable for on-the-fly analysis and exploration. It provides a real-time, responsive way to manipulate visualizations without committing to permanent changes in the data. For example, you might apply a table calculation filter to dynamically compute moving averages, percent changes, or other aggregations based on the current display, offering a more interactive and exploratory data analysis experience.

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In addition to these six major types of filters in Tableau, you may have to use other types of filters. Some of them are discussed below.

Quick filter:

Quick Filters provide a swift and accessible means to implement various filter types in Tableau. By simply right-clicking on a field, users can efficiently access different filtering options. It helps you to quickly access different filter types in Tableau through the right-click option. It owns features required to fulfill all typical filtering needs. You can implement Quick filters in Tableau on measures or dimensions.

This feature is particularly handy for users who want a fast and uncomplicated way to interactively explore and analyze their data.

Global filter:

It can be used over multiple worksheets by using the same source data in a workbook. Moreover, it can be used on all worksheets by utilizing the same data. This ensures consistency in data representation throughout various sheets. Changes made to the filter criteria in one place automatically propagate to affect all related sheets, promoting coherence and efficiency in data analysis.

Global Filters are valuable for maintaining uniformity and avoiding discrepancies in data visualization across different parts of a workbook.

 Cascading filter:

It allows for the selections in the first filter to modify the options in the second filter. So, it restricts the values to those that are only significant to the first filter. Moreover, it avoids users from choosing irrelevant data. Hence, it offers an improved user experience. In other words, the Cascading Filter helps users focus on specific subsets of data by tailoring available options based on their initial selections.

This functionality is particularly beneficial when dealing with large datasets or complex data structures, as it streamlines the process of narrowing down choices and avoids overwhelming users with irrelevant information.

 User filter:

Its alternate name is row-level security. This filter in tableau restricts and administers the data that users can view or access depending on the authority specified. Essentially, the User Filter allows administrators to define rules and permissions that determine which data rows individual users or groups are permitted to access. By associating specific users or groups with particular data filters, Tableau ensures that users only see the subset of data they are authorized to view.

This functionality is particularly crucial when dealing with confidential or sensitive information, as it enables organizations to implement strict access controls, aligning with privacy and compliance standards.

Conclusion

This is how different types of filters in tableau work in various processes.

Hence, the various types of filters in Tableau are like handy tools that play a crucial role in different parts of working with data. They help us dig out important insights, make our visualizations clearer, and focus on specific details. Whether we’re using context filters for a closer look or top N filters to narrow down our focus, Tableau gives us the freedom to adapt our approach.

By working with these different types of filters in Tableau, your data exploration can be smooth and interactive experience, making your analyses more efficient and impactful. These filters become our reliable companions as we navigate through the world of data, making our Tableau journey more insightful and enjoyable.

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