OpenAI Data Analysis: From a Question to Checked Results

OpenAI data analysis works best when you give the model a defined question, let an appropriate tool perform the calculation, and verify the result against the source. A persuasive explanation alone is not evidence that the right rows or formula were used. fenbs can keep the assumptions, source references, and accepted findings outside the analysis session so the next assistant starts with the same understanding.

The workflow below applies to an OpenAI-powered analysis tool with code execution. For the app-specific steps, use ChatGPT for data analysis; this guide focuses on the work record and checks that should survive whichever interface you choose.

How do you start with a question you can verify?

1. Write the decision the analysis will inform. For example: decide which service category needs a closer review of late deliveries.
2. Define one calculation: late orders divided by eligible completed orders, grouped by service category and calendar month.
3. Name the source version, date range, exclusions, and person who can confirm the definitions.
4. Ask the assistant to describe its proposed method before running it. Resolve missing definitions before accepting a chart.

A request such as “find interesting trends” invites many plausible answers with no agreed stopping point. A narrower question lets you compare the output with a known total and decide whether the result actually changes your next action. Begin with a descriptive result before asking for possible explanations.

OpenAI documents Code Interpreter↗ (opens in a new tab) as a tool that lets models write and run Python code in an isolated environment. In an application built with the OpenAI API, the interface developers use to call the service, the developer must provide the tool; generating text does not itself prove that code ran.

What should you prepare before sharing the data?

Make a working copy of the source and retain the original. Remove fields the analysis does not need, check whether the tool is approved for the remaining information, and give the copy a recognizable version name. Do not add confidential rows merely because the assistant asks for more context.

Provide a short data dictionary: what one row represents, what each relevant column means, the units, and how blanks differ from zero. An order-level export and an order-line export can look similar while producing different counts. Tell the assistant which identifiers are expected to repeat.

• Check row counts, date coverage, duplicate identifiers, and totals before analysis.
• Specify the time zone and whether the end date includes the whole day.
• Keep amounts numeric and explain refunds, canceled records, and partial completions.
• List known gaps, including categories that do not report consistently.

These preparations also make a later agile workflow easier to review: the task describes a bounded question with explicit evidence, rather than an open-ended request to improve reporting. Record the definitions in fenbs AI context so they remain available after the file is replaced.

How do you ask for an inspectable analysis?

Split the work into inspection, calculation, and interpretation. First ask for the file structure and quality problems. Then ask for the agreed calculation with an intermediate results table. Only after those checks should you request a chart and a short explanation of what it shows.

Ask to see the code or query, the filters applied, and the number of records remaining after each important exclusion. Request a list of assumptions introduced during the work. An assumption such as treating a missing delivery date as late can change the result more than the choice of chart.

For example, tell the assistant: “Calculate the monthly late-delivery rate using the supplied definitions. Show eligible orders, late orders, and the rate for every month. Do not fill missing delivery dates or infer a cause. Flag any definition you cannot apply.” This gives you concrete items to inspect.

OpenAI’s prompt engineering guide↗ (opens in a new tab) explains the value of explicit instructions and relevant context. Treat a reusable instruction as a starting point, then supply the current source version and business definitions each time.

Which checks matter before you trust a result?

Recalculate one group independently in a spreadsheet or a trusted query. Reconcile the total eligible records with the input and account for every exclusion. Ask whether the average is calculated from individual records or from already averaged groups, because those methods can produce different answers.

Test awkward cases deliberately: a blank date, a repeated identifier, a refund, and a record exactly on the reporting boundary. Check chart labels against the results table. A correctly calculated percentage can still be presented with the wrong denominator or an incomplete period.

Separate observations from explanations. “The rate increased in the supplied records” may be supported; “the new supplier caused the increase” requires additional evidence. Ask what alternative explanations remain, and name the human reviewer responsible for accepting the finding.

Broader regression checks belong in OpenAI evaluation for workflows, while business-specific interpretation belongs in OpenAI finance workflows. Keep those separate from the basic calculation review.

What should survive when the analysis session ends?

Keep the source version, the calculation artifact, the reviewed result, and the next action together by reference. Store datasets and executable analysis in their appropriate systems; the project record needs a clear description and links that the next reviewer can open. Keep unreviewed results visibly separate from accepted findings.

OpenAI’s building agents documentation↗ (opens in a new tab) describes code execution with file inputs and generated files. Download and retain the outputs you need through your application’s supported workflow rather than relying on a past conversation as the only location.

A handoff should say what was checked, what changed after review, and which source to use next. The same record helps when comparing a result with Claude for data analysis. Spreadsheet-specific setup is covered separately in OpenAI in Excel workflows.

How to do this with fenbs?

1. Create a free account and add a project for the analysis you need to repeat.
2. Add a task containing the question, the expected calculation, and source references. Keep raw datasets in the approved analysis system.
3. Record the metric definitions a person agreed in Decisions and rules. Leave disputed assumptions as open questions.
4. Save the checked result, reviewer, remaining limitations, and next step in the task and Where we left off.

fenbs does not run Python, validate a statistical model, or import an OpenAI conversation. It fits when you need the reviewed work to continue across sessions; a single disposable calculation may need only a saved workbook. Our workflow automation tools guide covers the wider process, and the AI at work hub connects related workflows.