You can create a group to combine related members in a field. For example, if you are workingwith a view that shows average test scores by major, you might wantto group certain majors together to create major categories. English andHistory might be combined into a group called Liberal Arts Majors,while Biology and Physics might be grouped as Science Majors.
Groups are useful for both correcting data errors (e.g., combining CA, Calif., and California into one data point) as well as answering "what if" type questions (e.g., "What if we combined the East and West regions?).
Create a group
There are multiple ways to create a group. You can create a group from a field in the Data pane, or by selecting data in the view and then clicking the group icon.
Create a group by selecting data in the view
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In the view, select one or more data points and then, on the tooltip that appears, click the group icon 
.
Note: You can also select the group icon on the toolbar at the top of the workspace.
If there are multiple levels of detail in the view, you must select a level to group the members. You can select to group all dimensions, or just one.
Create a group from a field in the Data pane
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In the Data pane, right-click a field and select Create > Group.
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In the Create Group dialog box, select several members thatyou want to group, and then click Group.
The selected members are combined into a single group. A defaultname is created using the combined member names.
To rename the group, select it in the list and click Rename.
Tip: You can search for members using theFind option near the bottom-right of the dialog box. (Tableau Desktop only)
Include an OtherGroup
When you create groups inTableau, you have the option to group all remaining, or non-grouped members in an Other group.
The Include Other option is useful for highlighting certain groups or comparing specific groups against everything else. For example, if have a view that shows sales versus profit product category, you might want to highlight the high and lowperforming categories in the view, and group all the other categories into an "Other" group.
| Includes Other | Does not include Other |
| | |
To include an Other group:
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In the Data pane, right-click the group field and select Edit Group.
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In the Edit Group dialog box, select Include 'Other'.
Edit a Group
After you have created a grouped field, you can add and remove members from the groups, create new groups, changethe default group names, and change the name of the groupedfield. You can make some changes directly in the view, and others through the Edit Group dialog box.
To add members to an existing group:
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In the Data pane, right-click the group field, and then click Edit Group.
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In the Edit Group dialog box, select one or more members and drag them into the group you want.
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Click OK.
To remove members from an existing group:
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In the Data pane, right-click the group field, and then click Edit Group.
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In the Edit Group dialog box, select one or more members, and then click Ungroup.
The members are removed from the current group. If you have an Other group, the members are added to it.
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Click OK.
To create a new group in a group field:
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In the Data pane, right-click the group field, and then click Edit Group.
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In the Edit Group dialog box, select one or more members, and then click Group.
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Click OK.
Note: To rename a group, select the group in the Edit Group dialog box, and then click Rename.
See also
Color a View Using Groups(Link opens in a new window)
Correct Data Errors or Combine Dimension Members by Grouping Your Data(Link opens in a new window)
Other articles in this section
FAQs
Ronald Coase's statement, "If you torture data enough, it will confess to anything you like," highlights the idea that data can be manipulated or misinterpreted to support a wide range of conclusions or viewpoints, often to the detriment of objective and accurate analysis.
Which is the best example of a good question for data analysis? ›
The questions you want to ask in this stage are: Which data sources does my organization work with? Do I have the required permissions or credentials to access the data? What is the size of each dataset and how much data will I need to get from each one?
What makes a good data question? ›
The Key To Asking Good Analytical Questions
Data Dan: First of all, you want your questions to be extremely specific. The more specific it is, the more valuable (and actionable) the answer is going to be.
What is big data with answers? ›
Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before.
What is data torturing? ›
Data Torturing Definition. ▪ When the data analysis process goes beyond reasonable. interpretation of the facts, it becomes data torturing.
What is a famous quote about big data? ›
“Without big data, you are blind and deaf in the middle of a freeway.” “All models are wrong, but some are useful.” “The most valuable commodity I know of is information.” “Good Big Data teams will be very tolerant of “failure”.
What are the 3 most common data analysis? ›
The four types of data analysis are: Descriptive Analysis. Diagnostic Analysis. Predictive Analysis.
What are the three 3 kinds of data analysis? ›
There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.
How would you manage messy data? ›
How do you tackle big, messy data sets?
- Define your goals and questions.
- Explore and understand your data.
- Clean and transform your data.
- Analyze and model your data.
- Visualize and communicate your results.
- Review and refine your process. Be the first to add your personal experience.
- Here's what else to consider.
Data is a collection of information gathered by observations, measurements, research or analysis. They may consist of facts, numbers, names, figures or even description of things. Data is organized in the form of graphs, charts or tables.
Data sufficiency means checking and testing a given set of information to see if it is enough to answer a given question. These are designed to test the candidate's ability to correlate every provided question to reach a conclusion.
What are the 5 qualities of data? ›
There are five traits that you'll find within data quality: accuracy, completeness, reliability, relevance, and timeliness – read on to learn more. Is the information correct in every detail? How comprehensive is the information?
What is big data simple words? ›
Big data describes large and diverse datasets that are huge in volume and also rapidly grow in size over time. Big data is used in machine learning, predictive modeling, and other advanced analytics to solve business problems and make informed decisions.
What are the 3 types of big data? ›
Big data can be classified into structured, semi-structured, and unstructured data. Structured data is highly organized and fits neatly into traditional databases. Semi-structured data, like JSON or XML, is partially organized, while unstructured data, such as text or multimedia, lacks a predefined structure.
Why is data important? ›
Data allows organizations to more effectively determine the cause of problems. Data allows organizations to visualize relationships between what is happening in different locations, departments, and systems.
What is a famous quote about data analysis? ›
Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.
What is a famous quote about human cruelty? ›
Cruelty is a part of nature, at least of human nature, but it is the one thing that seems unnatural to us. Cruelty is, perhaps, the worst kid of sin. Intellectual cruelty is certainly the worst kind of cruelty. Gentleness is the antidote for cruelty.
Who said data is like garbage? ›
Mark Twain had a lot to say about statistics. He famously said: “there are lies, damned lies, and statistics.” He also said: “facts are stubborn, but statistics are more pliable.” And he said: “data is like garbage.
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