How to query a database in plain English
Updated 2026-07-01
Querying a database usually means writing SQL and then building a chart somewhere else. An AI data analyst collapses that into one step: you ask in plain English, it plans the query, runs it on your connected data, and shows you a chart plus the numbers behind it.
Here is how the flow works and how to get reliable answers.
- 1
Connect your data source
Connect a database, a spreadsheet or a file. The analyst reads the schema so it knows what tables and columns are available.
- 2
Ask a business question
Type what you want to know: "monthly revenue this year", "which regions grew fastest last quarter", "repeat purchase rate by cohort". Business language is fine — you don't need table or column names.
- 3
Review the query plan
On anything non-trivial, the analyst shows a plain-English plan — the sources, joins, filters and aggregation it will use — with the exact SQL one click away. Review it and approve, so you trust the answer instead of guessing at a black box.
- 4
Read the chart and refine
You get an auto-selected chart, the underlying table, and a short written insight. Refine by chatting: "add profit margin", "only the top 3", "group by month". It keeps the full conversation in context.
- 5
Save, pin or export
Pin the chart to a dashboard, save the analysis as a report, or export to CSV, Excel, PDF or slides. It's end-to-end — you never switch tools to finish the job.
How to ask questions that get good answers
- Ask for an outcome ("revenue by month"), not a table name.
- Add the time frame you care about ("this year", "last 90 days").
- Refine in steps rather than one giant question.
- If a field is ambiguous, clarify it once — the analyst can remember your definition for next time.
Why the query plan matters
Plain-English-to-SQL is only trustworthy if you can see what it did. Showing the plan and the exact SQL before running turns the analyst from a black box into something you can verify — which is what makes it safe to use for real decisions.
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