Use AI to Analyze Data: A Practical Workflow With Checks

To use AI to analyze data, define the question, prepare a permitted copy of the data, and ask a tool that can calculate to show its method and results. Independently check the important numbers before making a decision. A persuasive explanation does not establish that the assistant used the right rows or the right definition.

The analysis also needs a record outside the chat. fenbs can keep the business question, field definitions, approved assumptions, and next checks together, so a later session can continue the work without quietly changing what the numbers mean.

Where should you start before uploading anything?

1. Write the decision you need to support and one question the data can reasonably answer.
2. Identify the dataset, owner, date range, and what one row represents. Work on a copy and retain the original.
3. Confirm that the chosen tool and account are approved for this data. Remove fields that are unnecessary for the question.
4. Define the calculation and a result you can check manually before asking for a larger analysis.

For example, say a small service company wants to know which types of jobs most often finish after their promised date. A useful question compares late completed jobs with all completed jobs in each job type. A request to find insights leaves the assistant to invent both the question and the definition of success.

Write the definition of late explicitly. Does it mean a date later than the original promise, the most recent revised promise, or a scheduled appointment? Those definitions can produce different results from the same file. Have the responsible person choose the meaning before treating an analysis as an answer.

What kind of AI tool does this task need?

Choose by the work the tool must actually perform. For a small spreadsheet question, you may need file inspection and executable calculations. For a company reporting system, you may need an approved connection to governed data. A chat response that describes a calculation is different from evidence that a calculation ran.

OpenAI's data analysis documentation↗ (opens in a new tab) describes file analysis, tables, charts, and code-backed calculations in ChatGPT. Check availability for your account and ask to inspect the calculation method. Do not assume the same capabilities exist in every AI app or conversation.

For recurring reporting, business definitions matter as much as the interface. Microsoft's Power BI data preparation guidance↗ (opens in a new tab) describes preparing the underlying business data model and instructions to reduce ambiguity for Copilot. That preparation helps explain what a field means; it does not guarantee a correct answer.

Role-specific workflows belong in AI for data analysts and AI for data scientists. Here, focus on answering one work question with checks you can understand.

How should you prepare the file and definitions?

Use descriptive column names and consistent units. Make sure a row has one clear meaning, such as one completed job, rather than mixing job records with monthly totals. Check dates, category labels, missing values, and duplicate identifiers before asking for a comparison.

Keep a short data dictionary: a list that defines each field used in the analysis. For the service company, it might define job ID, job type, promised date, completion date, and status. Say whether a missing completion date means unfinished, unavailable, or something else. The assistant should not decide that distinction silently.

• Record counts: how many rows exist before and after filtering?
• Coverage: which dates, locations, or teams are included and excluded?
• Units: are durations hours or days, and are amounts in the same currency?
• Categories: do different spellings refer to the same group?
• Missing data: what is absent, and how will those rows be handled?

If the real work is maintaining data across systems, see AI in data management. Data entry with AI and data extraction with AI address getting information into a usable form before this analysis begins.

What should you ask the assistant to calculate?

Ask for an inspection before an interpretation. Have it report the columns, row count, date coverage, missing values, and unexpected categories. Compare those observations with what you know about the file. Stop if it misunderstood the row meaning or cannot read a relevant column.

Then request a specific calculation. For the service company, ask it to filter completed jobs in the agreed period, flag completion after the chosen promised date, and calculate late jobs divided by completed jobs for each job type. Require the numerator and denominator beside each percentage.

Ask it to show the code, formulas, or saved query it used and identify excluded rows. If you do not understand the method, request a plain-language explanation and have someone qualified review it before relying on the result. Keep the generated analysis files with the dataset so the work can be repeated.

Save the agreed definitions and remaining questions alongside the task. fenbs keeps project context and decisions available to your next connected assistant, reducing the chance that a new chat silently uses a different denominator.

Which checks matter before trusting the result?

Recalculate a small sample independently with a spreadsheet, calculator, or a separate reviewed method. Check totals against the source system or a trusted report. A second explanation from the same assistant is not an independent calculation.

Try an alternative reasonable definition when it could change the decision. Compare original and revised promised dates, for example, and explain the difference. Do not remove an unusual record just because it makes a chart less tidy; investigate whether it is an error or a real event.

Finish with a limited conclusion: what the data supports, which uncertainty remains, and what you recommend checking next. A job type having more late work does not by itself prove that the workers, customers, or scheduling process caused the delay.

How to do this with fenbs

1. Create a free account and add a project that states the business question and the decision it will inform.
2. Save field definitions and source references as project context. Keep the actual data and calculation files in their appropriate controlled locations.
3. Add a task with the analysis plan and required checks. Record the definitions a person approved in Decisions and rules, keeping unanswered choices open.
4. Record the checks you actually completed in the task's testing fields. Save Where we left off with remaining uncertainty and the next action.

fenbs keeps the work record; it does not calculate over your dataset or replace an analytics platform. If the whole job is a single calculation you can review immediately, your spreadsheet may be enough. The shared record helps when assumptions and follow-up work must survive several sessions.

What should the final analysis handoff contain?

Provide the question, source version, definitions, method, checked results, and limitations. Include the calculation files and the next check or human decision. Someone reviewing the handoff should be able to identify both how you got the number and why you think it answers the question.

That reusable record is one job of an AI brain. For more practical tasks built around the same habit of asking and checking, see using AI at work and the AI at work hub.