An uploaded file does not prove that information reached the right record or field. Define the scope first, then compare source and destination records to expose omissions, duplicates and unexplained differences.
Start by defining which records you will compare
A success message after moving a spreadsheet into another system is reassuring. Usually, however, it only means that the technical operation finished. You still need to check that information was assigned to the right customer, document and date. Reconciliation compares the expected source data with the resulting destination records under the same rules. The aim is to explain every difference, rather than label every difference an error.
Write down the transfer scope before checking: the file version, date range and record types included. Are cancelled records, empty rows and previously transferred information excluded? Changing these decisions later can produce different results from the same data. Keep an unchanged source copy and record when the operation took place.
Read the row count alongside record identifiers
Equal row counts are a useful first signal, but do not establish correctness. One record may have been duplicated while another was omitted. Choose a distinguishing key such as a job number, document number or suitable combination of fields. A person's name should not automatically be treated as unique.
If the destination generates a new identifier, retain its mapping to the source key. This helps locate missing records and identify what a rerun should update. Do not assume repeated source keys will be merged correctly. Agree with the process owner whether to merge, reject or create separate records.
Make field mappings and transformation rules explicit
For bulk data entry, prepare a short mapping showing source columns alongside destination fields. Include rules for dates, numbers, currencies, units and missing values. Removing leading zeroes can change the meaning of a code; interpreting a decimal separator differently can turn a populated cell into an incorrect value.
Comparison need not always mean character-for-character equality. An agreed rule may trim whitespace or convert a date into an equivalent format. Define that transformation beforehand. Do not invent new normalization rules during checking to conceal unexpected differences. Missing information, zero and unknown are not interchangeable values.
Check totals and business relationships
Totals help validate numerical fields, provided you specify the records included. Do not combine different currencies or measurement units into one total. State when rounding occurs. Equal totals still do not replace record-level checking: two incorrect values can cancel each other out.
Check relationships separately. Do order lines belong to the correct order? Is an attachment associated with the right customer? Does a completed job contain all required information? File-format validation cannot answer these questions. Turn everyday business rules into observable checks and try them on a small, representative sample first.
Record exceptions instead of silently skipping them
A report should show rejected, incomplete and review-required records as well as successful ones. For each exception, include the source key, field, expected and actual value, and next action. Readers should understand why the record needs attention. Restrict access and detail appropriately when fields contain sensitive information.
It may be appropriate to reject a record with a missing address and explicitly report it. Guessing the address is a different operation requiring an agreed rule. Preserve the system error and add a short explanation in business language. After a correction, make it possible to see which affected records were checked again.
Take care: reruns must not create more duplicates
If a connection fails and processed records are unknown, resending the entire file may create duplicates. Design a way to distinguish completed, failed and pending operations. Will a rerun update an existing record, skip it or stop? Test the observed result instead of relying only on a written description.
Define rollback and permissions before changing live data. Minimize or mask unnecessary personal and commercial information in test samples. Do not place checking files in a publicly accessible folder. If source data changes during reconciliation, freeze it or define how changes are accounted for; otherwise you compare two different moments.
Write acceptance criteria before the transfer starts
Report the scope, compared records, explained differences and outstanding exceptions together, rather than simply saying the transfer is complete. The process owner should decide acceptable differences beforehand. The approach described in our automation acceptance testing article applies here: an expected result must be observable and reproducible with the same sample.
URIKEM starts with your actual workflow. Tell us which file supplies which system, and how you check the result today. Once the scope is agreed, transfer and verification can be designed together. The delivery can then include a clear record of what the software did, alongside the ability to write data.
Frequently asked questions
Do equal row counts prove that a transfer is correct?
No. Duplicated and missing records can cancel each other out numerically. Compare unique keys, fields and relevant business rules too.
Is every different value an error?
No. An agreed formatting or rounding transformation may explain it. Document the rule and do not hide unexpected differences during validation.
Should the entire file be resent after an error?
First establish which records were processed. Test source-to-destination mappings and update or skip rules so that reruns do not create duplicates.
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