You no longer need to know SQL, DAX, or Python to analyse your business data. AI-powered analytics tools now let you type questions in everyday language — “what were our top products last quarter?” or “why did revenue drop in March?” — and get instant charts, tables, and insights in return. But the quality of answers depends entirely on how you ask. A vague question gets a vague answer. A well-structured question gets a useful one. This tutorial shows you how to write natural language queries that actually work, with practical examples you can adapt to your own data and tools.
What You’ll Need
Before starting, you need access to at least one AI analytics tool with natural language query (NLQ) capabilities. Any of these will work for this tutorial:
- Julius AI (free tier available — best for spreadsheet uploads)
- Power BI Copilot (requires Fabric or Premium capacity)
- ThoughtSpot (free developer plan or Essentials tier)
- Zoho Analytics with Zia (free plan available)
- ChatGPT with Advanced Data Analysis (Plus subscription, $20/month)
You will also need a dataset. For practice, use any spreadsheet you already have — a sales report, marketing campaign export, website analytics download, or customer list. The more familiar you are with the data, the easier it is to verify whether the AI’s answers are correct.
→ Not sure which tool to pick? Read our guide: AI for Non-Technical Teams: Data Tools That Don’t Require a Data Scientist
Step 1: Start With Simple, Verifiable Questions
The biggest mistake new users make is asking complex analytical questions before they have established whether the tool understands their data correctly. Start simple.
Your first three queries should be questions you already know the answer to. This is not about getting insights — it is about building trust. If the tool gets basic facts wrong, you need to fix the data connection or model before relying on it for real analysis.
Good starter queries:
- “How many rows are in this dataset?”
- “What is the total revenue for January 2026?”
- “How many unique customers do we have?”
- “What are the column names in this data?”
Why this matters: Every NLQ tool translates your plain English into a database query behind the scenes. If the tool misinterprets “revenue” as “profit” or reads dates in the wrong format, you will get confidently wrong answers. Catching these issues early — with questions where you know the correct answer — prevents costly mistakes later.
Example in practice: Upload a quarterly sales spreadsheet to Julius AI. Ask: “What is the total value in the Revenue column?” Compare the answer to your Excel SUM formula. If they match, you know the tool is reading your data correctly. If they do not, check whether the tool misidentified the column or mishandled currency formatting.
Step 2: Learn the Anatomy of a Good Natural Language Query
AI analytics tools perform best when your questions follow a clear structure. Think of every query as having up to four components:
Metric + Dimension + Filter + Time Period
Here is what that looks like in practice:
| Component | What It Is | Example |
|---|---|---|
| Metric | What you want to measure | Revenue, units sold, customer count, conversion rate |
| Dimension | How you want it grouped | By product, by region, by sales rep, by channel |
| Filter | What to include or exclude | Only enterprise accounts, excluding returns, UK customers only |
| Time period | When | Last quarter, January 2026, year-over-year, last 30 days |
Weak query: “Show me sales data.” Problem: No metric, no dimension, no time period. The tool has to guess what you want.
Strong query: “Show me total revenue by product category for Q1 2026.” Clear metric (revenue), dimension (product category), and time period (Q1 2026).
Even stronger query: “Show me total revenue by product category for Q1 2026 compared to Q1 2025, sorted by the biggest year-over-year increase.” Adds a comparison period and a sort order, giving you an immediately actionable answer.
The more specific your question, the more useful the answer. You are not having a casual conversation — you are writing instructions for a machine that translates your words into database queries. Precision pays off.
Step 3: Master Comparison and Trend Questions
Once you trust the basics, move to the queries that deliver real analytical value — comparisons and trends. These are the questions that previously required an analyst to build a custom report.
Time comparisons (period over period):
- “How did revenue this month compare to the same month last year?”
- “Show me weekly customer sign-ups for the last 12 weeks as a line chart.”
- “What was the month-over-month growth rate for each product line in 2025?”
Segment comparisons (dimension vs dimension):
- “Compare average order value between new customers and returning customers.”
- “Which sales region had the highest growth rate last quarter?”
- “Show me support ticket volume by priority level for the last 30 days.”
Ranking queries:
- “What were our top 10 products by revenue last quarter?”
- “Which three sales reps had the lowest conversion rate in March?”
- “Rank our marketing channels by cost per acquisition, lowest to highest.”
Pro tip: When a comparison query returns surprising results, follow up immediately with a “why” question. Most modern NLQ tools can handle drill-downs: “Why did the North region outperform the South in Q1?” or “What drove the revenue increase in the Enterprise segment?” Tools like ThoughtSpot’s Spotter and Power BI Copilot can generate automated explanations for anomalies. Julius AI will run statistical analysis to identify contributing factors.
Step 4: Ask for Specific Visualisation Formats
AI analytics tools will choose a chart type for you — but their default choice is not always the best one. Specifying the visualisation format in your query produces better results.
Use these phrases to control output format:
- “as a line chart” — for trends over time
- “as a bar chart” — for comparing categories
- “as a pie chart” — for composition breakdowns (use sparingly)
- “as a table” — when you need exact numbers, not visuals
- “as a heatmap” — for spotting patterns across two dimensions
- “as a scatter plot” — for relationship analysis between two variables
Practical examples:
- “Show me monthly revenue for 2025 as a line chart.”
- “Compare revenue by region as a horizontal bar chart, highest to lowest.”
- “Give me a table of all customers with more than £10,000 in lifetime value, sorted by most recent purchase date.”
- “Show the correlation between marketing spend and new customers as a scatter plot.”
When to override the AI’s chart choice: If the tool gives you a bar chart but you need a trend line, simply follow up with “show that as a line chart instead.” Most tools handle format conversion in a single follow-up query without losing the underlying data selection.
Step 5: Build Multi-Step Analyses Through Conversational Queries
The most powerful feature of modern NLQ tools is conversational context — the ability to ask follow-up questions that build on previous answers. This turns a single query into a complete analysis workflow.
Here is a five-query analysis sequence you can replicate with your own data:
Query 1 (Overview): “What was total revenue by product category for Q1 2026?” Establishes the baseline.
Query 2 (Comparison): “How does that compare to Q1 2025?” The tool remembers you are looking at product categories and revenue.
Query 3 (Drill-down): “For the category with the biggest decline, show me monthly revenue for the last 12 months.” Identifies the problem area and its trajectory.
Query 4 (Root cause): “What changed in that category — did we lose customers or did average order value drop?” Asks the tool to decompose the decline into contributing factors.
Query 5 (Action): “Show me the top 10 customers in that category who haven’t ordered in the last 90 days.” Converts analysis into an actionable re-engagement list.
In five questions, you have gone from a high-level overview to a specific list of customers to contact. This sequence would have taken an analyst hours to build as a custom report. With conversational NLQ, it takes minutes.
Important caveat: Not all tools handle multi-step context equally well. ThoughtSpot’s Spotter maintains context across long conversation chains. Power BI Copilot handles 2–3 follow-ups reliably but can lose context on longer sequences. Julius AI maintains context within a session but may need reminding of earlier filters. If a follow-up query returns unexpected results, restate the full question with all filters rather than relying on the tool’s memory.
What to Expect: Realistic Results
AI analytics tools in 2026 handle straightforward queries with high accuracy — factual lookups, aggregations, comparisons, and rankings work reliably across all major platforms. Where accuracy drops is on queries requiring complex business logic, multi-table joins that were not pre-configured, or questions that require the tool to infer definitions the data does not explicitly contain.
A practical rule of thumb: if a query would take a competent analyst less than 5 minutes to answer in a spreadsheet, the AI will almost certainly get it right. If it would take an analyst 30 minutes of exploration and judgement, verify the AI’s output before acting on it.
Always sense-check results that surprise you. When the AI tells you something unexpected, that is either the insight you were looking for — or an error in how the query was interpreted. Investigate before you act.
FAQ
Do I need to know my database schema to ask good questions? No — but knowing your column names helps. Most tools display available fields when you start typing. If you are working with uploaded spreadsheets, use clear column headers (e.g., “Revenue” instead of “Col_F”) to help the AI understand your data.
What if the AI gives a wrong answer? Rephrase your question with more specificity. Instead of “show me sales,” try “show me total revenue in the Sales Amount column for the date range January 1 to March 31, 2026.” If the tool consistently misinterprets a term, check whether your data model uses different naming — the AI can only work with the labels it can see.
Can I use natural language queries for live dashboards? Yes, on enterprise platforms. ThoughtSpot, Power BI Copilot, and Zoho Analytics all allow NLQ queries against live-connected data sources. Julius AI and ChatGPT work with uploaded file snapshots rather than live connections on standard plans.
AI Agent Brief helps professionals find the right AI tools for their business. Our tutorials are based on hands-on testing across multiple platforms. We may earn affiliate commissions from links on this page — this does not affect our editorial independence.
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