Analyzing data with ChatGPT

Analyzing data with ChatGPT | OpenAI

April 10, 2026

OpenAI Academy

Analyzing data with ChatGPT

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

Loading…

Share

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.

How to get started

1. 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. 2. 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. 3. 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. 4. If visuals would help, request them explicitly—what to plot, how to segment, and any must-haves like axis labels or units. 5. Ask for outputs you can reuse such as a clean final table or a short executive summary that translates findings into action.

Task

Context

Expected output

Analyze this data and summarize key insights.

Use the sample dataset from our Shopify store (last 30 days).

Provide structured summary of key insights, including what stands out across channels and products, identification of underperforming areas (e.g., low-converting channels), and notable patterns. Includes 4–6 prioritized observations and 5 specific follow-up analyses or questions to investigate next.

Review and analyze our sales funnel data.

Use the data from [Campaign name] from [connected analytics app].

Produce set of clearly separated sections: (1) key observed patterns in the funnel, (2) hypotheses explaining those patterns (e.g., onboarding as primary driver), and (3) recommended experiments or tests. Insights are ranked by business impact, with emphasis on conversion bottlenecks and leverage points.

Identify issues or inefficiencies in a process using data

Review the attached current process document, as well as the support team ticket data CSV.

Output a prioritized list of operational issues and bottlenecks (e.g., escalation delays, repeat ticket drivers), each supported by data signals. Includes clear reasoning for why each issue matters, plus recommended areas for immediate improvement or investigation, grouped into quick wins vs deeper fixes.

Tips for success

• 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.

See more: OpenAI Help Center: Data analysis⁠

KPI dashboard summaryAnalyze this spreadsheet and produce: (1) a KPI table (MoM + YoY), (2) 3 charts, and (3) a 10-bullet exec summary with ‘risks’ and ‘recommended actions’. Data: [upload].

Marketing: campaign performance reviewYou are a marketing analyst. Using these datasets [upload], identify the top 3 drivers of conversion changes, compute conversion rate by channel, and recommend 5 optimizations. Present results as a table + short narrative.

Finance: anomaly detectionScan this export for anomalies (duplicates, unexpected spikes, vendor changes). Flag the rows to review, explain why each is suspicious, and suggest the next check I should run. Data: [upload].

Operations: forecastingUsing this historical volume data, build a simple forecast for the next 8 weeks. Show the model choice, assumptions, and a confidence range. Then summarize what staffing implication this has. Data: [upload].

Turn analysis into slidesConvert these findings into 5 slide titles + bullets (audience: exec staff). Include 1 chart per slide and a final ‘decision needed’ slide. Findings: [paste or link].

KPI dashboard summaryAnalyze this spreadsheet and produce: (1) a KPI table (MoM + YoY), (2) 3 charts, and (3) a 10-bullet exec summary with ‘risks’ and ‘recommended actions’. Data: [upload].

Marketing: campaign performance reviewYou are a marketing analyst. Using these datasets [upload], identify the top 3 drivers of conversion changes, compute conversion rate by channel, and recommend 5 optimizations. Present results as a table + short narrative.

Finance: anomaly detectionScan this export for anomalies (duplicates, unexpected spikes, vendor changes). Flag the rows to review, explain why each is suspicious, and suggest the next check I should run. Data: [upload].

Operations: forecastingUsing this historical volume data, build a simple forecast for the next 8 weeks. Show the model choice, assumptions, and a confidence range. Then summarize what staffing implication this has. Data: [upload].

Turn analysis into slidesConvert these findings into 5 slide titles + bullets (audience: exec staff). Include 1 chart per slide and a final ‘decision needed’ slide. Findings: [paste or link].

KPI dashboard summaryAnalyze this spreadsheet and produce: (1) a KPI table (MoM + YoY), (2) 3 charts, and (3) a 10-bullet exec summary with ‘risks’ and ‘recommended actions’. Data: [upload].

Marketing: campaign performance reviewYou are a marketing analyst. Using these datasets [upload], identify the top 3 drivers of conversion changes, compute conversion rate by channel, and…