Analyzing data with ChatGPT

Analyzing data with ChatGPT

Explore, analyze, and turn data into clear insights and actions.

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ChatGPT can help you move from raw data to useful insights with minimal setup. You can upload a CSV or Excel file, paste in a table, or connect a data source (if supported in your workspace), then start asking questions in plain language.

Instead of building formulas, pivot tables, or dashboards for every question, you can quickly explore data, clean up tables, generate simple visualizations, and extract key takeaways in a format that's easy to share.

It’s especially useful early in the process—when you’re still figuring out what’s in the data, identifying anomalies, and deciding where to dig deeper. It also helps translate findings into summaries others can review and act on.

- Start with the decision you’re trying to support. A simple frame is: “I’m trying to decide ___, based on ___.” This tells ChatGPT what “done” looks like and keeps the analysis focused.

- Provide your data along with any critical context—definitions, timeframe, and what key columns represent. You can provide data via file upload, or by using a connected app.

- Ask for an approach, not just an answer. or example, request an exploratory data analysis (EDA) summary followed by hypotheses to test. This leads to more structured and reliable results than jumping straight to conclusions.

- If visuals would help, request them explicitly—what to plot, how to segment, and any must-haves like axis labels or units.

- Ask for outputs you can reuse such as a clean final table or a short executive summary that translates findings into action.

- Help ChatGPT help you by sharing what “good” looks like up front including what success metric you care about, the timeframe you’re looking at, and which groups or segments you want to compare.

- If the numbers really matter, you can also ask it to show how it got there including the assumptions it made, any formulas it used to calculate metrics, and quick checks for missing data or unusual spikes.

- It also helps to set a few simple ground rules so the analysis stays trustworthy. For example, you can tell it not to treat correlations as causes, to point out any limitations in the data, and to flag anything that looks off. And before you share results or make a decision, do a quick reality check—pick a couple key numbers and spot-verify them to make sure everything adds up.

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