Analyse data

Tools such as Julius write and run real analysis code, which is more reliable than a model guessing totals from text. Ask to see the code or formula behind any number you plan to act on. Check one figure by hand, such as a monthly total, before you share a chart. Clean column names and one header row cut most mistakes.

ChatGPT analyses files and data inside the assistant you may already pay for. Claude handles long documents and careful reasoning well, and it has a free plan to test your file. Julius is built only for data, so it returns charts, tables and summaries by default and can connect to a database. Run the same question through two of them and keep the one whose answer you can check fastest.

A chat tool covers one-off questions on a file. Move to Hex once you query a data warehouse with SQL and want the analysis to rerun with fresh data. Finished Hex notebooks publish as interactive apps, so colleagues get answers without writing code. It has a free plan, but it suits teams with someone comfortable in SQL or Python.

Limits depend on the tool and plan, so check the upload cap before you export a full year of orders. For large data, connect a database instead of uploading a file: Julius and Hex both work that way. Remove columns you do not need, which speeds things up and keeps personal data out. Split very large exports by month if an upload fails.

Customer names, emails and order histories are personal data under GDPR, so you need a data processing agreement with the vendor. Check whether your plan lets the vendor train on your uploads, and switch that off where you can. Remove or replace names and emails when the question does not need them. Totals by region or product rarely need anyone's identity.