MIDA / AI & AUTOMATION

What does AI need to give useful answers about your data?

Schema, definitions and access boundaries matter as much as the question.

Give the model business context

A field called revenue does not explain whether it includes tax, refunds or cancelled orders. Supply a concise metric dictionary, table descriptions and the reporting grain. Describe which timestamps and currencies apply. Provide a few approved questions and queries so the intended calculation is clear.

Keep calculations reproducible

Use explicit SQL or another deterministic calculation for reported totals. AI can draft a query or explain checked results, but a plausible explanation is not evidence that the calculation is correct. Verify the joins, filters and aggregates against known results before using the answer in a report.

Start with narrow permissions

Begin with a controlled dataset and read-only access. Limit the tables and fields to the reporting purpose. Assess personal records and confidential commercial data separately before sending them to a model provider. A public inquiry form is not the place to share credentials or customer-level data.

Test questions that expose mistakes

Prepare cases with returns, missing data, many-to-one joins, timezone boundaries and multiple currencies. Include a question the dataset cannot answer. Check whether the response identifies insufficient information instead of inventing a number. Retain the expected result and the reason behind it.

Expand after reviewing the evidence

Track incorrect answers, manual corrections and time spent reviewing. Keep the data period, source and definition attached to an explanation. Mida’s AI-assisted service follows this approach; the public Data Workbench itself uses defined calculations and does not send uploaded files to an AI service.