As Microsoft Copilot becomes a central feature in Power BI, organizations are increasingly asking a critical question:
Is our data actually ready for AI?
Many Power BI datasets work perfectly well for dashboards and reports, but they are not always optimized for natural language queries used by AI tools like Copilot. When your data model isn’t prepared properly, Copilot may misunderstand questions, return incorrect results, or fail to answer queries altogether.
One of the most effective ways to prepare your data model for AI-powered analytics is by using synonyms.
In this guide, we’ll explore:
- Why Copilot sometimes struggles with Power BI datasets
- How synonyms improve AI understanding
- Where to configure synonyms in Power BI
- A real example of how synonyms transform Copilot responses
If you’re learning Power BI or improving your data modeling skills, structured training can help accelerate the process. You can explore professional learning resources
Why Data Preparation Matters for Copilot
When Copilot interacts with your Power BI model, it interprets questions using natural language processing (NLP). However, Copilot can only understand what exists inside your data model’s metadata.
If your dataset is not optimized, Copilot may produce responses like:
- “I don’t understand the request.”
- It may reference the wrong column
- It may generate unexpected visualizations
In many cases, this is not a Copilot error it’s a data preparation issue.
As AI becomes more integrated with analytics, the role of the Power BI data analyst is evolving. Analysts must now prepare datasets not only for reporting but also for AI consumption.
The Future of Q&A in Power BI
The traditional Power BI Q&A visualization is scheduled to be deprecated in December 2026.
However, this doesn’t mean the functionality behind Q&A is disappearing. Instead, it is being integrated into Copilot and AI-driven experiences within Microsoft Fabric and Power BI.
This means the techniques used to optimize Q&A—like synonyms and semantic modeling—are becoming even more important.
Think of Copilot Like an Intern
A helpful way to think about Copilot is this:
Copilot is like a new intern at your organization.
It understands Power BI concepts and data modeling, but it doesn’t understand your company’s internal terminology.
For example:
Your company might refer to:
- Resellers as partners
- Customers as accounts
- Revenue as bookings
Unless Copilot is taught these relationships, it may not understand user questions.
This is exactly where synonyms come in.
What Are Synonyms in Power BI?
Synonyms are alternative names or phrases assigned to tables, columns, and measures within a Power BI data model.
They allow Copilot and natural language queries to interpret different ways people might ask questions.
For example:
| Actual Field Name | Possible Synonyms |
|---|---|
| Reseller | Partner, Distributor, Friend |
| Revenue | Sales, Income |
| Customer | Client, Account |
By defining synonyms, you help Copilot understand different linguistic variations of the same concept.
Where to Manage Synonyms in Power BI
Synonyms are configured in Model View within the Q&A setup panel.
Steps to Configure Synonyms
- Open your Power BI model view
- Select Q&A Setup
- Navigate to the Synonyms pane
- Choose the table, column, or measure
- Add alternative terms users might ask for
The synonyms panel will display:
- Existing table names
- Column names
- Measures
- Suggested synonyms
- Approved synonyms
You can easily add your own custom terms that reflect how people actually speak inside your organization.
Example: When Copilot Works Perfectly
Imagine you ask Copilot the question:
“What are my total sales by reseller?”
If your dataset contains a Reseller table, Copilot will easily understand the request and generate a visualization showing:
- Sales by reseller
- A bar chart or table
- Sorted results for quick analysis
This works because the terminology matches the data model.
Example: When Copilot Gets Confused
Now imagine users inside your organization refer to resellers as “friends.”
A user might ask:
“What are my total sales by friends?”
If the word friends does not exist in your dataset, Copilot will likely respond with something like:
“I’m not able to answer that question.”
Again, the problem isn’t the AI—it’s the missing semantic context.
Fixing the Problem with Synonyms
To fix this issue, you can add “friends” as a synonym for the reseller column.
Example configuration:
Column: Reseller Name
Synonyms:
- Friends
- Friend
- Bros
Once the synonym is added, Copilot understands that “friends” refers to resellers.
Now when a user asks:
“What are my total sales by friends?”
Copilot will:
- Map “friends” to the Reseller Name column
- Retrieve the correct data
- Generate the appropriate visual
The result will match the same output as the original query.
Verifying Copilot Results
Power BI also allows you to inspect how Copilot generated the answer.
You can view:
- The generated DAX query
- The tables and columns used
- The logic Copilot applied to produce the visualization
This transparency helps analysts ensure the AI-generated insights are accurate and trustworthy.
Best Practices for Preparing Power BI Data for AI
To make Copilot perform at its best, follow these best practices:
1. Use Clear Naming Conventions
Avoid cryptic column names like:
cust_id
rev_amt
Instead use:
Customer ID
Revenue Amount
2. Add Business Language Synonyms
Think about how people actually speak.
For example:
| Field | Synonyms |
|---|---|
| Revenue | Sales, Income |
| Customer | Client, Account |
| Reseller | Partner, Distributor |
3. Avoid Ambiguous Synonyms
If multiple fields share the same synonym, Copilot may become confused.
Always ensure each synonym maps clearly to one concept.
4. Prepare Your Data Model
AI performs best with:
- Clean relationships
- Star schema models
- Well-defined measures
- Meaningful metadata
Why Synonyms Are Critical for “Chat With Your Data”
The goal of Copilot is to enable natural conversations with data.
Users should be able to ask questions like:
- “Show me revenue by region”
- “Which customers bought the most last quarter?”
- “Who are our top partners?”
Without synonyms, Copilot may fail to interpret these variations.
With synonyms, Copilot becomes significantly more flexible and intelligent.
Final Thoughts
As AI continues transforming business intelligence, data preparation is becoming just as important as visualization design.
Features like synonyms in Power BI help bridge the gap between structured data models and natural human language.
By teaching Copilot how your organization speaks about data, you can unlock powerful capabilities like:
- Conversational analytics
- Faster insights
- AI-driven reporting
If you’re serious about mastering Power BI and preparing your data for AI-powered analytics, consider structured learning resources like
