Data analysis
# Work Users
# Advanced Features
Use ChatGPT for data analysis
July 23, 2025 · Last updated on August 7, 2025ChatGPT for data analysis
ChatGPT lets anyone analyze data easily and quickly, without needing advanced technical skills. It helps surface insights, generates clear charts, and simplifies routine tasks like cleaning, combining datasets, and writing Python code. This enables everyday employees to independently uncover findings, freeing up your data specialists to handle more complex tasks.
Our models have been specifically trained to support data analysis tasks. The best results are typically seen when using reasoning models. They can proactively suggest insights, prompt deeper questions, and directly answer queries about your data. This capability empowers faster, more effective data-driven decisions across your teams.
Why it’s useful at work
- Ask questions in plain language Upload your data, then just ask your question. ChatGPT writes and runs the Python code for you.
- Simplify your data prep Easily clean your data, merge columns, spot anomalies, or create new fields, all with a single prompt.
- Explore trends visually ChatGPT can suggest useful questions, analyze your data, and generate interactive charts.
- Act on insights immediately Quickly share your findings or trigger actions like sending emails, creating JIRA tickets, or connecting to other apps directly from your chat.
Using data analysis in a workflow
- Gather inputs – connect to data warehouses, SaaS apps, or upload files
- Prepare data – clean, transform, and enrich the dataset
- Analyze data – run queries, create visuals, build models
- Deliver outputs – download files or send results through integrated GPT actions
Getting started
- Open a new chat.
- Import data by uploading a data file (ex. CSV or XLSX), or connect directly to Google Drive or OneDrive.
- Begin by asking simple questions like “Describe this data” to get quick insights.
- Continue refining your analysis with clear, plain-language prompts like “Show monthly revenue in a bar chart.”
- Interact with the generated tables and charts to explore trends or adjust visuals.
- When finished, download your findings or use GPT Actions to share results.
Tips on working with data
- Define metrics clearly from the start and confirm them with ChatGPT. Review generated code or output tables carefully.
- Always verify critical results yourself before sharing, since ChatGPT can make mistakes.
- Use ChatGPT for conversational data exploration. For fully automated workflows, leverage the OpenAI API.
Examples for your role
Role | Use case | Ready‑to‑use prompt |
Marketing | Campaign performance analysis, segmentation trends, A/B test summaries, channel attribution | Compare email campaign open rates over time |
Product | Feature usage trends, user feedback clustering, bug frequency tracking, experiment results | Show top 10 reported bugs by product area |
Finance | Budget variance analysis, sensitivity modeling, automated reconciliations, portfolio tracking | Run a sensitivity analysis on revenue projections |
Sales | Lead scoring, pipeline forecasting, win/loss trend analysis, territory performance | Forecast next quarter sales based on pipeline |
Engineering | System metrics review, error log pattern detection, deployment trend analysis | Analyze server error rates by day |
HR | Headcount tracking, attrition analysis, diversity metrics, pulse survey insights | Chart employee attrition by quarter and department |
IT | Incident pattern tracking, license usage review, helpdesk ticket classification | Summarize helpdesk tickets by category |
Please note that our recordings might be showing an older version of the ChatGPT UI
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