Let’s say you have data about college students with dozens of different majors – Computer Science, Software Engineering, Information Technology, Computer Engineering, etc. Instead of showing 50+ individual majors in your chart, wouldn’t it be cleaner to group them into categories like “Technology,” “Business,” “Sciences,” and “Liberal Arts”?
That’s exactly what grouping in Power BI does. And trust me, once you learn this technique, you’ll use it everywhere.
Why grouping in Power BI matters (the real-world problem)
The messy reality: Your data often comes with too much detail for clean reporting. You might have:
- 47 different college majors when you need 5 broad categories
- 200+ product SKUs when you want to show product families
- Dozens of sales territories when you need regional views
- Individual cities, when you want country-level analysis
The clean solution: Grouping lets you create meaningful categories that tell a better story with your data.
Method 1: Visual-level grouping in Power BI(the quick way)
This is the fastest method when you just need to group data for one specific chart.
Step-by-step visual grouping in Power BI:
Step 1: Create your visual
- Add a chart (bar chart, pie chart, whatever works)
- Put your detailed category (like “Major”) in the Axis or Legend field
Step 2: Select items to group
- In your visual, hold Ctrl and click on the items you want to group together
- For example, select “Computer Science,” “Software Engineering,” “Information Technology”
Step 3: Right-click and group
- Right-click on your selection
- Choose “Group” from the context menu
- Power BI creates a new group automatically
Step 4: Rename your group
- The group will have a generic name like “Computer Science and 2 others.”
- Right-click the group and select “Rename group.”
- Give it a meaningful name like “Technology”
Step 5: Repeat for other categories
- Select Business Administration, Marketing, Finance
- Group them as “Business”
- Continue until all items are grouped
Visual grouping pros and cons:
Pros:
- Super fast and easy
- No data modeling required
- Perfect for one-off analysis
Cons:
- Only affects that specific visual
- Doesn’t save the grouping for reuse
- Limited to simple groupings
Method 2: Field-level grouping in Power BI(the reusable way)
This method creates permanent groups you can use across multiple visuals.
Step-by-step field grouping:
Step 1: Go to the Fields pane
- Find your category field (like “Major”) in the Fields pane on the right
- Right-click on the field name
Step 2: Create a new group
- Select “New group” from the context menu
- A “Groups” dialog box will open
Step 3: Set up your groups
- You’ll see all the unique values from your field listed
- Select multiple items (hold Ctrl) that belong together
- Click the “Group” button
- Rename the group to something meaningful
Step 4: Create all your groups
- Technology: Computer Science, Software Engineering, IT, etc.
- Business: Business Admin, Marketing, Finance, etc.
- Sciences: Biology, Chemistry, Physics, etc.
- Liberal Arts: English, History, Philosophy, etc.
Step 5: Handle ungrouped items
- Any items you don’t manually group go into “Other.”
- You can rename “Other” to something more specific
- Or group remaining items into additional categories
Step 6: Use your new grouped field
- Power BI creates a new field called “Major (groups)”
- Use this in any visual instead of the original field
- Your groups will appear consistently across all visuals
Method 3: DAX calculated columns (the flexible way)
When you need complex grouping logic or conditions, DAX gives you ultimate control.
Basic DAX grouping in Power BI:
Major Category =
SWITCH(
TRUE(),
Students[Major] IN {“Computer Science”, “Software Engineering”, “Information Technology”, “Computer Engineering”}, “Technology”,
Students[Major] IN {“Business Administration”, “Marketing”, “Finance”, “Economics”}, “Business”,
Students[Major] IN {“Biology”, “Chemistry”, “Physics”, “Mathematics”}, “Sciences”,
Students[Major] IN {“English”, “History”, “Philosophy”, “Art”}, “Liberal Arts”,
“Other”
)
Advanced conditional grouping in Power BI:
Student Category =
SWITCH(
TRUE(),
Students[GPA] >= 3.5 && Students[Major] IN {“Computer Science”, “Engineering”}, “High Performing STEM”,
Students[GPA] >= 3.5, “High Performing Other”,
Students[Major] IN {“Computer Science”, “Engineering”}, “STEM”,
“General Student”
)
Real-world grouping in Power BI examples
Example 1: E-commerce product grouping
Original: 500+ individual products
Grouped by: Electronics, Clothing, Home & Garden, Sports, Books
Example 2: Sales territory grouping
Original: 47 states/provinces
Grouped by: North, South, East, West, Central
Example 3: Customer age grouping
Original: Individual ages (18, 19, 20, 21…)
Grouped by: Gen Z (18-26), Millennials (27-42), Gen X (43-58), Boomers (59+)
Example 4: Revenue grouping
Original: Exact revenue amounts
Grouped by: Small (<$1M), Medium ($1M-$10M), Large (>$10M)
Best practices for effective grouping in Power BI
Make groups meaningful
Good grouping: Technology, Business, Health Sciences, Liberal Arts
Bad grouping: Group A, Group B, Group C, Miscellaneous
Keep groups balanced
Avoid having one huge group and several tiny ones. If 80% of your data falls into “Technology,” consider breaking it down further.
Use consistent logic
If you’re grouping by industry, stick to industry categories. Don’t mix industry with geography or size.
Consider your audience
Group data in ways that make sense to the people viewing your reports. Academic departments might want different groupings than HR or admissions.
Document your grouping logic
Keep notes about how you defined each group, especially if the logic is complex.
Handling tricky grouping in Power BI scenarios
Overlapping categories
Some items might logically fit in multiple groups. Make a decision and stick with it consistently.
Example: “Business Information Systems” could be Technology or Business. Pick one and document your choice.
Seasonal or time-based grouping in Power BI
Season =
SWITCH(
MONTH(Sales[Date]),
12, “Winter”,
1, “Winter”,
2, “Winter”,
3, “Spring”,
4, “Spring”,
5, “Spring”,
6, “Summer”,
7, “Summer”,
8, “Summer”,
“Fall”
)
Dynamic grouping based on values
Revenue Category =
IF(
Sales[Revenue] > 100000, “High Value”,
IF(Sales[Revenue] > 10000, “Medium Value”, “Low Value”)
)
Grouping with multiple conditions
Sometimes you need to group based on multiple factors:
Student Segment =
SWITCH(
TRUE(),
Students[Year] = “Senior” && Students[GPA] >= 3.5, “High Achieving Senior”,
Students[Year] = “Senior”, “Senior”,
Students[GPA] >= 3.5, “High Achieving Underclassman”,
Students[International] = “Yes”, “International Student”,
“General Student”
)
Visual tips for grouped data
Use consistent colors
When showing grouped data, use consistent colors across all visuals. Technology should always be blue, Business always green, etc.
Sort groups logically
Don’t just use alphabetical order. Sort by importance, size, or logical flow.
Show group totals
Consider adding totals or percentages to show the size of each group.
Drill-down capability
Set up drill-down from groups to individual items so users can see details when needed.
Common grouping mistakes to avoid
Mistake 1: Too many groups
Having 15+ groups defeats the purpose. Aim for 3-8 meaningful categories.
Mistake 2: Uneven group sizes
One group with 90% of the data and others with 2% each isn’t helpful.
Mistake 3: Confusing group names, “Technology-Related Fields” is clearer than “Tech Stuff.”
Mistake 4: Not handling nulls. Always account for blank or missing values in your grouping logic.
Mistake 5: Inconsistent grouping across reports. If Computer Science is “Technology” in one report, it should be “Technology” everywhere.
Advanced grouping in Power BI
Hierarchical grouping
Create multiple levels of grouping:
- Level 1: STEM vs Non-STEM
- Level 2: Technology, Engineering, Sciences vs Business, Liberal Arts
- Level 3: Individual majors
Grouping with measures
Performance Group =
IF(
AVERAGE(Students[GPA]) > 3.0,
“Above Average Major”,
“Below Average Major”
)
Time-based dynamic grouping
Group data differently based on the time being viewed.
Testing your groups
Verify completeness: Make sure every item in your original data is assigned to a group.
Check logic: Review edge cases and make sure grouping makes sense.
User testing: Show grouped reports to actual users and get feedback.
Data validation: Compare totals between original and grouped data to ensure nothing was lost.
Maintaining groups over time
New data considerations: When new majors or categories appear, update your grouping logic.
Regular reviews: Periodically review groups to ensure they still make business sense.
Version control: Document changes to the grouping logic over time.
Integration with other Power BI features
Slicers: Grouped fields work great in slicers for filtering
Drill-through: Set up drill-through from groups to detailed views
Bookmarks: Save different grouping views using bookmarks
Row-level security: Apply security at the group level rather than individual items
Real impact of good grouping in Power BI
I’ve seen reports go from cluttered messes with 50+ categories to clean, actionable dashboards with 5-6 meaningful groups. The difference in user adoption is dramatic.
In Nepal’s growing data community, organizations like NEST Nepal often showcase examples of how proper data categorization and grouping can transform complex datasets into clear business insights. It’s one of those fundamental skills that separates amateur reports from professional analytics.
Getting started with grouping in Power BI today
Start simple: Pick one overly detailed field in your current report and try visual-level grouping.
Think business logic: Ask yourself – how would my audience naturally think about these categories?
Iterate: Your first grouping attempt might not be perfect. Refine based on feedback.
Document decisions: Keep notes about why you grouped things the way you did.
The key is remembering that grouping in Power BI isn’t just about making charts look cleaner (though it does that). It’s about helping people understand patterns and insights that get lost in the noise of too much detail.
Good grouping turns data into information, and information into actionable insights. That’s the real power of this seemingly simple technique.
