Inconsistent column names across files

• The export template was changed between one period and the next.
• Each department or supplier built its own version of the sheet.
• Headers were retyped by hand, with different spacing, case, and abbreviations.

• Stacking the files produces several partly filled columns where one full column was expected.
• A script that looks for a column by name fails, or quietly skips files that lack it.
• Columns that share a name but not a meaning are merged, and the mix is hard to detect later.

• List the headers of every file side by side before combining anything.
• After stacking, look for columns that are empty for whole files.
• Compare the kind of values under each header, not just the header text.

• Define one standard set of column names, with a short description of what each holds.
• Map every header in every file to a standard name, and keep the mapping so the next batch can reuse it.
• Decide from the values, not the name, when two headers look alike. Where the values do not settle it, ask whoever produced the file.
• Rename before combining, so the files stack on matching columns.

• One table split across sheetsRows that belong in one table are spread over several sheets or files, one per month, region, or person.
• Merged and multi-row headersThe column names are spread over two or more rows, often with merged cells, so no single row names every column.
• The same entity in two systemsOne person or organization exists in two systems under different identifiers, with nothing linking the records.
• Case example: Marketing analyticsFive clients, five definitions of a campaign

TermsSchema, Schema drift, Mapping, Data dictionary