ChatGPT makes data analysis easier: a practical guide | Keryc
ChatGPT can turn tables and files into useful answers without you having to build pivot tables or complex formulas. Do you have a CSV, an Excel file, or a table pasted into the chat? Upload it, give context and start asking in natural language. You’ll see how to go from raw data to actionable insights with very little setup.
What you can achieve with ChatGPT when analyzing data
Quickly explore what’s in a dataset and spot anomalies without writing a lot of code.
Clean columns, unify formats and prepare a reusable final table.
Generate executive summaries anyone on your team can review.
Ask for simple visuals (bar charts, time series, segmentations) to guide decisions.
Prioritize observations and suggest next steps or experiments based on business impact.
Does it sound like magic? Not necessarily. It’s especially useful at the initial stage: understanding what the data contains and deciding where to dig deeper.
Best practices to ask for reliable analysis
Start with the decision. Use the frame: I'm trying to decide ___, based on ___. That tells ChatGPT what “ready” looks like.
Provide context: column definitions, date ranges, what each metric represents. Without context, numbers are misread.
Ask for an approach, not just an answer. Request a brief exploratory summary (EDA) followed by hypotheses to test. That structures the analysis.
Request visuals with requirements: what to chart, how to segment, axis labels and mandatory units.
Ask for reusable outputs: a clean table, a final CSV or an executive summary ready to share.
Calculation transparency: if numbers matter, ask for the formulas and assumptions used to compute metrics and a small check for missing data or unusual spikes.
Clear rules: tell it not to confuse correlation with causation, to point out limitations and to flag suspicious data.
A practical tip: before presenting results, manually verify 2 or 3 key figures to make sure everything adds up.
Prompt templates you can use right now
General analysis and priorities:
"Analyze this dataset from our Shopify store (last 30 days). Give me 4–6 prioritized observations and 5 follow-up analyses to investigate. Indicate which channels or products look underperforming."
Sales funnel:
"Review these sales funnel data from campaign X. Split the response into: (1) key patterns, (2) hypotheses that explain them, (3) recommended experiments. Order by business impact."
Operational efficiencies:
"Review this CSV of support tickets and the current process document. Return a prioritized list of operational issues with the data evidence, indicating quick wins vs deep fixes."
Simple framework to ask for confidence evaluation:
"Summarize the analysis and show the formulas used. State the assumptions, missing data and any values outside expectations."
Practical step-by-step flow
Prepare the data: clean column names, normalize dates and remove simple duplicates.
Upload the file or paste the table; if you have connections, link the source.
Define the decision and the success metric (for example, increase conversion on channel X from 1.2 to 1.8 in 30 days).
Ask for a brief EDA: distributions, outliers and summary tables by segment.
Request actionable hypotheses and concrete experiments, prioritized by impact and effort.
Ask for a final table and a 3-sentence executive summary to share by email.
With this flow you reduce noise and get results the team can quickly review.
Quick example (Shopify store)
Suggested prompt:
"Analyze the Shopify sales dataset for the last 30 days. Give me 4 prioritized observations, each with the key evidence (metric and segment), and 5 follow-up questions or analyses we should run. Include a final CSV with cleaned columns: date, channel, product, sales, visits, conversion_rate."
What you might receive:
Observation 1: Paid channel A has 3x more visits but 40% lower conversion than organic. Evidence: rates by channel.
Observation 2: Product B shows a high return rate in a specific segment. Evidence: returns by SKU and cohort.
Observation 3: Sales spikes on weekends concentrated in category X.
Observation 4: Many rows with null dates in the sessions table; possible tracking loss.
Follow-ups suggested by ChatGPT:
Compare conversion rate by device for paid channel A.
Review return policies and reasons in support tickets for Product B.
Run an A/B test on the paid channel landing page.
Validate the event pipeline to fix null dates.
Segment cohorts by first contact channel and measure initial LTV.
This kind of response gives you immediate observations and actionable tasks.
Risks and how to mitigate them
Privacy and security: don’t upload sensitive data if you’re not authorized. Anonymize when necessary.
Bias and missing data: ChatGPT only interprets what you give it. If important records are missing, conclusions can be wrong.
Overconfidence: use results to guide hypotheses, not to make final decisions without verification.
Warning signs: lots of nulls, inconsistent units (for example mixing dollars with another currency), spikes that align with tracking changes.
Always finish with a manual check of some figures and, if possible, validate findings with another source or a colleague.
Reflective closing
ChatGPT doesn’t replace the analyst or quality controls, but it can speed up exploration, communication and prioritization. If you know which decision you want to support and give clear context, you’ll get more useful, actionable analyses. Ready to try with your first CSV?
