Mapping Columns to Fields
A mapping says which part of an incoming file becomes which field in your data.
Where to find it
Architect Panel → Data:
- File Stores — the stores themselves, and View File Store on a row
- Large Uploads — in-progress and stalled upload sessions
- File Import Routines — watched sources and their mappings
Architect Panel → Data:
- Datastores — the fields being mapped to
Map by name where you can
Mapping by column position breaks the moment the source adds a column, and it breaks silently — every field after the insertion is shifted, so the data is wrong rather than absent.
Mapping by header name survives reordering and fails visibly when a column disappears, which is what you want.
Decide what happens to unmapped columns
Ignored, or an error. Ignoring is convenient and means a new column carrying something important arrives unnoticed. For anything where the source might change, failing is safer.
Types are where imports go wrong
- Dates — establish the source’s format explicitly. Day-first and month-first data looks identical for twelve days a month.
- Numbers — decimal separators, thousands separators, and negative values in brackets.
- Leading zeros — account numbers and postcodes that a spreadsheet has helpfully removed.
- Encoding — accented characters arriving as symbols.
Validate before writing
A required field empty, a value outside its range, a reference that matches nothing. Catching those at import means one file in quarantine; catching them afterwards means finding and correcting rows across your data.
Decide about existing records
Whether the import creates, updates or both, and what identifies a match. An import that creates duplicates because the matching field was not unique is the most common import failure, and it is very tedious to unpick.
Import a sample first
Ten rows, then look at them properly — not just that they arrived, but that every field holds what it should. Then run the rest.
Keep the source file
At least for a while. When somebody questions imported data, the file is what settles it, and it is the only way to distinguish a mapping problem from a source problem.
Re-check when the source changes
A supplier changing their export format will not tell you. Anything importing from outside your organisation deserves a periodic check that the mapping still matches what is arriving.
Worked example
An organisation maps by header name, fails on unrecognised columns, and states the date format explicitly. A ten-row sample was checked field by field before the first full run, which found postcodes losing their leading zeros in the supplier’s export.
Recommendations
- Map by name, never by position.
- State the date format explicitly.
- Import ten rows and inspect them before the rest.
- Keep source files to settle later questions.