Combining files is the easy half. The hard half arrives immediately afterwards, when three exports that each looked fine turn out to disagree about how a date is written and what the column is called.
Method 1: Power Query, for files with the same shape
Keep the Source.Name column. It is the only way to trace a bad row back to the export it came from.
Power Query is strict about column names. If one file says Email and another says E-mail, they arrive as two different columns with half the rows blank in each.
- 1 Put every file in one folder, and nothing else.
- 2 In Excel, open the Data tab, choose Get Data, then From File, then From Folder.
- 3 Select the folder and click Combine, then Combine and Load.
- 4 Excel builds a query that appends every file and adds a Source.Name column so you can tell which row came from which file.
- 5 When a file is added to the folder later, click Refresh and the sheet updates.
Method 2: copy and paste, for a handful of files
For three or four files, opening each one and pasting under the last is honestly faster than building a query. Two rules make it survivable: paste values only, so no formatting or formulas come along, and add a column naming the source file before you paste.
Check the row count as you go. A paste that silently lands one row short is the classic way to lose a record without noticing.
Method 3: concatenate the CSVs
If the files are already CSVs with identical headers, you can join them from a terminal without opening Excel at all. On macOS or Linux:
head -1 first.csv > combined.csv && tail -n +2 -q *.csv >> combined.csv
That keeps one header line and appends every data row. It is fast on files too large for Excel to open comfortably, and it does nothing clever, which is exactly what you want at this stage.
When the files do not have the same columns
This is where most merges go wrong, and it is worth handling inside the query rather than in the merged sheet afterwards.
Power Query appends by column NAME. Email and E-mail are two columns to it, so an append of three files can produce a sheet with six half filled columns instead of three full ones. The same happens when one export has an extra column the others do not: the missing values arrive as null rather than as an error, so nothing warns you.
- 1 After the combine step, open the query in the editor rather than loading it straight to a sheet.
- 2 Rename the mismatched columns so every source uses one name, before the append step runs.
- 3 Check the column count against what you expected. A merge that produced more columns than any single file is telling you the names disagree.
- 4 Look at the row count too. It should be the sum of the source files, minus one header per file.
Column names are the easy half of the disagreement. The values inside them are the half that shows up later, which is the next section.
The part that actually takes the time
Once the rows are in one sheet, the mismatches surface all at once. The same customer appears in two of the source files. One export wrote dates as 04/03/2026 and another as 4-Mar-26. One system exported amounts with a currency symbol and another as plain numbers. One file was saved as Latin-1 and now the accented names are damaged.
None of that is visible file by file, and all of it breaks the moment you pivot the combined sheet.
This is the point to run the merged file through a cleaning pass: normalize the dates and numbers, repair the encoding, and dedupe across the sources. A data cleaning tool that proposes each of those changes as a line you approve matters more here than usual, which matters here because a merged file is exactly where a silent rewrite does the most damage. For the fields most likely to disagree, see address standardization.