Analyzing Data with ChatGPT

ChatGPT lets you quickly start data analysis through CSV or Excel file uploads, pasting tables, or connected data sources where supported. Without immediately building complex formulas, pivot tables, or dashboards, you can explore the structure and key patterns in your data using only natural language questions.

It is especially useful in the early stages of analysis. It's well suited for understanding what's in the data, finding outliers or missing values, and deciding where to look deeper, and it also helps organize results into a summary that others can review right away.

When getting started, it's best to first clarify the purpose of the decision, and provide context together such as the data's definitions, time period, and the meaning of key columns. Rather than simply asking for an answer, requesting an EDA summary and hypotheses to test first produces results that are more structured and reliable.

For practical use, the following approach is recommended:

• Specify the visualizations you need concretely: including what to show and with which axes and units.
• Ask for reusable outputs: a cleaned final table, a short executive summary, and prioritized observations.
• To increase confidence in the results, also check the calculation methods, assumptions, and checks for missing values or outliers.

It's also important to instruct it not to interpret correlation as causation, and to explicitly state the data's limitations and anomalies. Finally, performing a spot check by manually cross-checking a few key numbers helps catch errors before the results are used for actual decision-making.