Natural Language Q&A in Microsoft Fabric and Power BI

Natural language Q&A capabilities in Microsoft Fabric and Power BI allow users to ask questions about their data in plain English and receive instant visualizations and answers. This democratizes data access across your organization. Learn more about Copilot integration for enhanced AI capabilities.

What is Natural Language Q&A?

Power BI Q&A is a natural language processing feature that interprets questions typed in everyday language and returns answers as charts, cards, or tables. With the addition of Copilot, these capabilities have been significantly enhanced with generative AI.

Key Features of Q&A in Power BI

Ask Questions in Plain English Type questions like "What were total sales last quarter?" or "Show me top 10 customers by revenue" and get instant visualizations without writing any DAX or building reports manually.

Automatic Suggestions As you type, Q&A suggests questions based on your data model, helping users discover insights they might not have thought to ask about. This guided experience makes analytics accessible to non-technical users.

Teach Q&A Your Terminology Configure synonyms and phrasings so Q&A understands your business terminology. If your company calls revenue "bookings," you can teach Q&A to recognize this term.

Featured Questions Curate a list of suggested questions for your reports. These appear as quick-start options, guiding users to the most valuable insights. See our guide on Power BI report design for UX best practices.

Setting Up Q&A for Success

1. Optimize Your Data Model Q&A works best with well-designed star schema models. Use clear, descriptive names for tables and columns. Avoid abbreviations and technical naming conventions.

2. Configure Linguistic Schema Define synonyms for columns and tables. For example, map "revenue," "sales," and "income" to your actual measure names. This improves Q&A accuracy significantly.

3. Add Row Labels Specify which columns contain row labels (like product names or customer names). This helps Q&A understand how to filter and group data correctly.

4. Review Q&A Suggestions Regularly check what questions users are asking. Use this feedback to improve your linguistic schema and add missing synonyms.

Q&A vs Copilot: Understanding the Difference

| Feature | Q&A | Copilot | |---------|-----|---------| | Question interpretation | Rule-based + ML | Generative AI | | Response type | Single visualization | Multiple visuals + narrative | | DAX generation | No | Yes | | Report building | No | Yes | | Available in | All Power BI licenses | Premium/PPU |

Best Practices for Natural Language Analytics

For Report Designers - Use business-friendly column names (not technical IDs) - Create explicit measures for common calculations - Configure the linguistic schema thoroughly - Test with non-technical users before publishing - Document common questions and their expected results

For End Users - Start with simple questions, then refine - Use the suggestion dropdown for guidance - Pin useful Q&A visuals to dashboards - Provide feedback when results aren't accurate - Explore "Show me" and "What is" question patterns

Copilot Enhancement to Q&A

With Copilot in Power BI, natural language capabilities are supercharged:

Conversational Follow-ups Ask follow-up questions that reference previous context. "Now show that by region" works because Copilot remembers your conversation.

Narrative Summaries Request explanations like "Explain why sales dropped in Q3" and get AI-generated insights based on your data patterns.

Report Generation Describe what you want to see: "Create a report showing sales performance with regional breakdown and trend analysis" and Copilot builds it.

Common Q&A Issues and Solutions

Issue: Q&A doesn't understand my question - Solution: Add synonyms to the linguistic schema, use clearer column names

Issue: Wrong measure is being used - Solution: Create explicit measures with clear names, configure preferred aggregations

Issue: Dates aren't recognized correctly - Solution: Mark your date table, ensure proper date hierarchies exist

Issue: Q&A returns "I don't have data for that" - Solution: Check data model relationships, verify the data exists in your dataset

Measuring Q&A Adoption

Track these metrics to understand Q&A usage: - Number of Q&A queries per day/week - Query success rate (questions that returned results) - Most common questions asked - Questions that failed (opportunities for improvement)

Use Power BI usage metrics to monitor adoption across your organization.

Ready to implement natural language analytics? Contact our team for help optimizing your Q&A experience.