AI Data Analyst — AI Glossary

Reference details

Related terms

• Text-to-SQL
• Table Question Answering
• Code Generation

How it works

1. The schema or file structure is loaded as context — tables, columns, types, and ideally a description per field, since a column named `amt` carries no meaning the model can recover from the name.
2. The natural-language question is translated into executable code, SQL against a warehouse or a dataframe operation against a file. This is the step where the model interprets intent, and the interpretation is the thing that goes wrong.
3. The code is executed in a sandbox with a read-only connection, a row limit and a timeout, because generated code is untrusted input and an unbounded join is a self-inflicted outage.
4. Results are rendered — a table, a chart chosen from the shape of the output — and narrated in prose. The narration is generated from the result set, which means a correct narration of a wrong query reads exactly like a correct answer.
5. Errors are fed back for a bounded number of repair attempts. This reliably fixes syntax and wrong column names, and reliably does not fix a query that runs and answers a different question.
6. The durable systems show the generated code alongside the answer, because the only available check on intent is a human reading the query.

Example: Why execution success rate hides the errors that matter

Take 100 questions. Suppose 15 produce code that fails outright — a bad column name, a syntax error — and 85 execute. Of those 85, suppose 10 execute cleanly but answer a different question than was asked: an inner join that silently drops customers with no orders where the question said all customers. The headline figure is an 85% execution success rate. The figure that matters is that of the 85 answers delivered to a user, 10 are wrong, so 10 / 85 = 11.8% of everything presented as an answer is incorrect — and incorrect in the specific way that is hardest to notice, since the number is plausible and the narration is fluent. The 15 loud failures cost a retry and nothing else. Improving the error rate from 15 to 5 is visible progress that changes none of the risk; reducing the 10 is the entire safety problem, and the only mechanism that reliably does it is showing the generated query to someone who knows the schema.

Commonly confused with

An AI data analyst is not a BI tool with a chat box, and the difference is who is accountable for the query. A BI dashboard runs a query written and reviewed once by someone who knows the schema, then serves it repeatedly; the semantics were settled at build time. An AI analyst generates a new query per question, so the semantics are decided at read time by a model inferring intent from a sentence. That makes it excellent for exploration, where a wrong answer is cheap and visible, and risky as a source for reported metrics, where it is neither.

When to use it

Reach for it when:

• Exploratory analysis, where the cost of a wrong answer is a wasted minute and the next question corrects it.
• Well-documented schemas with meaningful column names, which is the main predictor of whether intent is recoverable.
• Teams where the generated SQL will actually be read, so the model's interpretation is reviewable.
• Unblocking non-technical users on questions they would otherwise queue for an analyst, with the understanding that queued questions got review.

Reach for something else when:

• Producing reported or regulated metrics, where the semantics must be fixed, reviewed and stable rather than re-derived per question.
• Against undocumented schemas with cryptic names, where neither the model nor a reviewer can verify intent.
• With a write-capable or unlimited database connection, which turns a wrong query into an incident rather than a wrong answer.
• Where the answer is consumed without the query being shown, which removes the only control on silent misinterpretation.

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