AI data analyst

An AI data analyst is an AI model that takes a question typed in plain words, writes the database query that answers it, runs the query, reads the result and explains it. It is also called an AI analyst or conversational analytics. When the model works through several steps on its own, it is sometimes called agentic analytics. Before such tools, a manager with a new question emailed an analyst, a person whose job is to work with data, and waited hours or days for the answer. An AI data analyst gives the answer in minutes, and shows how it got there.

Here is how it works in general. A company keeps its records in a data warehouse, a large database built for answering questions. A person types a question, such as "Which ten products had the most returns last month?" The model first reads a description of the warehouse: which tables exist, what each column means, and how the tables connect. Much of this description comes from the company's data catalogue. The model then writes a query. A query is a precise request in a database language, usually SQL. In plain words, this one says: "add up the returns for each product in October, and list the top ten". The model runs the query on the warehouse and reads the rows that come back. If the query fails, or the result looks wrong, the model reads the error, corrects the query and runs it again. AI coding agents work in the same loop of writing, running and correcting code. Finally, the model writes the answer in plain words, draws a chart and shows the query it used.

Showing the query matters. A language model can give a wrong answer in the same confident tone as a right one. This is called AI hallucination. Here, it usually means joining the wrong tables or counting the wrong thing. For example, "returns" may mean items sent back, or money refunded. So careful companies write down agreed definitions of their main numbers, and the model must use them. The model also sees only the data that the person asking is allowed to see. Each query uses the warehouse's computing, and the company pays for that computing.

Take, for example, a sales manager at a chain of pharmacies. She types "Why did cough syrup sales fall in Nagpur last week?" into a tool for asking the data in plain language. The answer is a stock shortage at two stores, and the query behind it is shown below the answer.

Consider an investment analyst studying a cement company. A financial research platform applies the same idea to documents instead of tables: its model reads filings and call transcripts, and answers with references to the pages it used.

Say a nightly data load fails at an insurance company. With data observability, a model reads the error and the warehouse's records, and writes a note on which source table changed.

How is an AI data analyst different from a business intelligence (BI) dashboard? An analyst builds a dashboard in advance, to answer the same questions every week, such as sales by city. An AI data analyst answers a new question on the spot, one that no dashboard was built for. Most companies use both: the dashboard for the numbers everyone checks, and the AI data analyst for the next question. A human analyst still checks the answers that matter most.