This article explains the checks the system performs before it queues a Timesheet Import batch for processing, how required field mappings are validated, how reimbursement items are identified, and what happens if data types do not match the mapped fields.
📌 Note: For a full list of fields that can be mapped and their data types, see mapping fields.
Validate Timesheet Import Field Mapping
When you queue a Timesheet Import batch for processing, the system validates the field mapping to make sure the mapped fields are sufficient to theoretically create timesheets and timesheet items in the Time and Attendance module. If any field mapping or data type validation fails, the batch is not queued and an error message explains why.
1. Check job or candidate mapping
The system first checks whether any of the following fields are mapped to identify the FastTrack job order for each row:
Job ID.
Job Alt No.
Purchase Order Number.
If none of these fields are mapped, the system then checks whether all of the following are mapped instead:
One of Candidate ID, Candidate Alt No, Candidate Full Name, or both Candidate Firstname and Candidate Surname.
Item Date or Week Ending Date.
If neither the job fields nor the required candidate/date combinations are mapped correctly, validation fails and the batch cannot be queued.
2. Check attendance item mapping
The system checks whether Start Time 1 and End Time 1 have been mapped. These fields are normally required to identify attendance items.
Standard rates timesheets may not have shift start and end times, so mapping Start Time 1 and End Time 1 is not always mandatory.
However, if a start time field is mapped, the corresponding end time field must also be mapped.
If Start Time 1 and End Time 1 are mapped, the system then checks that the following are also mapped:
Item Date.
One of Attendance Item Name, Attendance Item Import Code, Pay Code Name, or Pay Code Import Code.
If neither Start Time 1 nor End Time 1 is mapped, the system checks that all of the following fields are mapped instead:
Item Date.
Pay Code Name or Pay Code Import Code.
Quantity.
If the required attendance-related fields are missing, the batch fails validation.
3. Check additional shift start and end times
If Start Time 1 and End Time 1 are mapped, the system checks the additional shift fields:
Start Time 2 – Start Time 10.
End Time 2 – End Time 10.
For each additional start or end time field that is mapped, the system checks that the corresponding start/end field is also mapped.
If Start Time 2 is mapped, End Time 2 must also be mapped.
If End Time 10 is mapped, Start Time 10 must also be mapped.
If a start time is mapped without its matching end time (or vice versa), validation fails.
4. Check unpaid break fields
If Start Time 1 and End Time 1 are mapped, the system also checks any break fields:
Break Start 1 – Break Start 10.
Break End 1 – Break End 10.
For each break start or end field that is mapped, the system checks that the corresponding field is also mapped.
If Break Start 1 is mapped, Break End 1 must also be mapped.
If Break End 10 is mapped, Break Start 10 must also be mapped.
If a break start is mapped without its matching break end (or vice versa), validation fails.
5. Check reimbursement item mapping
To validate that a row represents a reimbursement item, the system checks that all of the following fields are mapped:
Item Date.
Reimbursement Item or Reimbursement Import Code.
Either Net and Tax (GST/VAT), or Currency Code and Currency Value.
When importing a reimbursement row:
Do not also include a value for Pay Code if a reimbursement field is mapped.
The Reimbursement Item or Reimbursement Import Code is what identifies the Reimbursement Pay Code.
If you also supply a Pay Code value for the same row, the system cannot determine whether you are importing a normal pay code or a reimbursement, and the import errors for that timesheet’s data.
Validate Data Types in Mapped Fields
When you queue a Timesheet Import batch, the system also validates that the type of data in each mapped column matches the data type of the FastTrack field it is mapped to.
For each mapped column, the system checks that:
Date fields contain dates in the correct format.
Time fields contain valid time values.
Numeric fields contain numeric data.
Text fields do not contain incompatible data types.
For example, if a column header is mapped to a field that must contain a date value, the system checks that the column contains dates rather than text or other data. If the date format is incorrect, a pop-up message is displayed, such as:
Error: Date contains an invalid format type for the field mapping. Required date format is DD/MM/YYYY.
If any data type check fails, the batch is not queued for processing until the import file is corrected and uploaded again.