A spreadsheet answer can be wrong even when its arithmetic looks convincing. The tool may have selected the wrong rows, combined currencies or treated a blank as zero. Before asking for a large analysis, build a small example whose expected result you can calculate yourself. That gives you a way to inspect the method as well as the final number.<\/p>\n\n\n\n
For example, you might want completed orders only, grouped by currency, with duplicate identifiers flagged for review. The blank amount should stay missing rather than becoming a zero-value sale. Those choices define the analysis more than the wording of the question does.<\/p>\n\n\n\n
Record the expected row count as well as the total. Two errors can sometimes cancel each other numerically. A matching total with the wrong number of included rows is a reason to investigate, not a successful check.<\/p>\n\n\n\n
If the tool proposes a formula or script, inspect it with the tiny example before applying it to your working file. Keep the original data unchanged so you can compare the result and reverse an unsuitable transformation.<\/p>\n\n\n\n
Watch for a title such as \u201cMonthly revenue\u201d when the table contains only a sample of completed orders. That title makes a broader claim than the data supports. Rename it to describe the actual scope rather than accepting the more impressive wording.<\/p>\n\n\n\n
Save the data dictionary, inclusion rules, example rows and expected results together. When a new column or currency appears, update the test before reusing the workflow. The objective is not to obtain a confident explanation of your data. It is to produce an analysis whose rows, assumptions and calculations you can account for.<\/p>\n\n\n\n