Correct Failed Import Rows¶
When an Excel import completes with errors, FieldOps keeps the successfully imported rows and records the rows that could not be processed.
You can review the failed rows, correct the underlying problems, and retry them without re-importing the rows that were already processed successfully.
Before you begin¶
Make sure that:
- The original Excel import has finished processing
- The import has one or more failed rows
- You have permission to review and retry failed import rows
An import containing failed rows will typically have the status Completed with errors.
How partial import success works¶
FieldOps processes rows individually.
For example, if an Excel file contains 100 rows:
- 92 rows may import successfully
- 8 rows may fail
The 92 successful rows remain imported.
Only the 8 failed rows need to be reviewed and corrected.
Important
Do not re-import the complete original Excel file solely because some rows failed. Correct and retry the failed rows instead.
Step 1: Open the failed rows¶
Open the failed row records associated with the import.
FieldOps records information about each row that could not be processed.
A failed row may include:
- The original Excel row number
- The error message
- The data contained in the row
- Additional information about the failure
Use this information to identify why the row could not be imported.
Step 2: Review the error message¶
Read the error message for each failed row.
The error message should help identify the value or condition that prevented the row from being processed.
Common causes may include:
- Missing required values
- Invalid data formats
- Invalid dates
- Values that cannot be processed
- Incorrect or inconsistent source data
For example, a row may fail because:
- A required value is empty
- A date is in an unexpected format
- A numeric field contains text
- A value does not match the expected field type
Tip
Review the error message before changing the source data. Correcting the specific problem reported by FieldOps reduces the chance of the row failing again.
Step 3: Correct the failed data¶
Correct the values that caused the rows to fail.
Keep the structure of the corrected data consistent with the original import.
For example:
| Problem | Original value | Corrected value |
|---|---|---|
| Invalid date | July 40, 2026 |
2026-07-14 |
| Number expected | forty-five |
45 |
| Missing required value | blank | Add the required value |
Correct only the data that needs to be fixed.
Warning
Do not change the meaning or structure of the data unnecessarily. The corrected rows should remain compatible with the mappings used by the original import.
Step 4: Prepare the corrected failed rows¶
Prepare the corrected data required by the failed-row retry workflow.
Include the rows that previously failed after correcting the reported problems.
Do not include rows that were already imported successfully.
Important
The retry workflow is intended for failed rows. Successfully imported rows do not need to be processed again.
Step 5: Re-upload the corrected failed rows¶
Use the available Re-upload Fixed Failed Rows action for the failed import.
Upload the corrected data according to the retry workflow.
FieldOps will process the corrected failed rows separately from the rows that were already imported successfully.
This allows you to correct import errors without repeating the entire original import.
Step 6: Wait for the retry to complete¶
The corrected rows are processed in the background.
Processing time may depend on:
- The number of corrected rows
- The amount of data being processed
- Background queue activity
Allow the retry process to complete before reviewing the results.
Step 7: Review the retry results¶
After processing is complete, review the results.
Confirm that:
- The corrected rows were processed successfully
- The expected visits or responses were created or updated
- No failed rows remain that still require attention
If some rows fail again, review the new error information and correct the remaining problems.
If a row fails again¶
A corrected row may fail again if:
- The original problem was not fully corrected
- Another invalid value exists in the same row
- The corrected value still does not match the expected format
- Required information is still missing
Review the latest error message and correct the remaining issue before retrying again.
Common problems¶
I cannot find the failed rows¶
Confirm that:
- The original import has completed processing
- The import contains failed rows
- You are working in the correct FieldOps organization
- You have permission to access import failures
I do not understand the error message¶
Review the row data alongside the reported error.
Look for:
- Missing values
- Incorrect data types
- Invalid dates
- Unexpected text in numeric fields
- Values that do not match the expected monitoring field type
If the problem is still unclear, collect the error message and relevant non-sensitive row information before requesting support.
The corrected row failed again¶
Review the latest failure information.
A row may contain more than one problem. Correcting one value may reveal another issue during the next processing attempt.
Should I upload the complete original Excel file again?¶
Normally, no.
If some rows were already imported successfully, use the failed-row correction workflow for the rows that failed.
This prevents unnecessary reprocessing of successful records.
Best practices¶
When correcting failed rows:
- Review the exact error before editing the data
- Correct only the rows that failed
- Preserve the expected column structure
- Keep data formats consistent
- Do not include successfully imported rows in the retry
- Review the retry results after processing
What to do next¶
After all failed rows have been corrected and successfully processed:
- Confirm that the expected records are available in FieldOps
- Review the imported visits and responses
- Continue with your operational workflow
- Use FieldOps reporting or BI access when required