AI for Data Analysts

Data analysts use AI to speed up four parts of their work: drafting SQL, cleaning data, exploring datasets and writing up findings. General assistants such as ChatGPT and Claude can turn a plain-English question into a query, and tools such as Microsoft Excel, Power BI, Tableau and many cloud data warehouses now offer natural-language querying and summaries. ChatGPT's data analysis feature, originally called Code Interpreter, writes and runs Python on an uploaded file, which makes quick exploration possible without a local setup. The core risk is that AI-generated analysis looks finished even when it is wrong. A query can run without errors and still answer a different question. Joining on a non-unique key duplicates rows and inflates totals, a filter can silently drop nulls, and a date condition can ignore time zones. When the model does not know your schema, it may invent plausible column or table names. Checking methods separate a professional from someone who only writes prompts. Reconcile outputs against a known total, such as monthly revenue from the finance report. Check row counts before and after each join. Read the generated SQL line by line and say in words what each clause does. Test on a small sample where you can compute the answer by hand. For exploratory findings, check whether a pattern survives segmentation, because aggregated data can reverse direction within subgroups, a pattern known as Simpson's paradox. Narrative reporting is another strong use, but AI may state causation where the data only shows correlation, or add explanations not found in the data. A common misconception is that AI removes the need to understand statistics. In practice it raises that need, because plausible-looking analysis is now cheap to produce.