Column mapping, explained

Attendance codes are usually a single ID with nothing to split, so the useful work is adding a readable timestamp and a status column you can filter. Keep one row per scan rather than one row per person: it gives you an audit trail, and a pivot table still gets you the daily count.

Nothing in the product alternates direction between scans, so use two scanner links named Clock in and Clock out pointed at the same spreadsheet and give both this mapping. The link name lands in the recorded_at_point column, which is what makes each row's direction explicit rather than inferred from scan order. Pair the rows and total the hours with formulas in your own sheet; this records presence, not a payroll timesheet.

For a stock count the barcode is the entire value, so no splitting is needed. Add the timestamp and the device so two people counting different aisles can be told apart later, which is the thing most counts get wrong.

This is the case column mapping was built for. One printed badge carries the ID, the name and the tier separated by a delimiter, and splitting it means the door list is readable without a lookup formula. Check which delimiter your badge printer actually uses before the event.

A loan log needs to answer who has what, right now. Keep the asset tag raw so it matches your asset register exactly, and rely on the timestamp order to work out whether a scan was a checkout or a return.

Visitor logs are often read during an evacuation, so the company matters as much as the person. Splitting the pass code from the company keeps both filterable, and a default keeps the column populated when a pass carries only an ID.

Tracking numbers are scanned fast and in bulk, so the value stays whole and the timestamp does the work. The device column tells you which van or bay a scan came from when two people are receiving at once.