Check multiple identifiers together.
Initialising algorithm engine for Bulk Checksum Validator...
How Bulk Checksum Validator Works?
The Bulk Checksum Validator checks each line against its expected check digit. It splits off the supplied digit, calculates it again and labels the line Valid or Invalid. Each result includes the expected value.
- 1Paste complete identifiers, 1 per line.
- 2Select the scheme or use automatic length detection.
- 3Recompute each line's check digit from its payload.
- 4Compare supplied and expected values.
- 5Filter, count, and export the invalid lines.
Bulk Checksum Validator Formula
The bulk validation rule is Resultᵢ = (lastᵢ = f(payloadᵢ)) for each line.
for i in 1…N: (BULK VALIDATOR)
Resultᵢ = valid if supplied_i = f(payload_i) else invalid
Error rate = invalid ÷ NNis the count of identifiers validated.
Valid = 5 Invalid = 2 Error rate = 28.6%
Bulk Checksum Validator Conversion Table
The bulk validator table shows 7 GS1 identifiers checked in 1 run.
| Line | Identifier | Supplied | Expected | Result |
|---|---|---|---|---|
| 1 | 5901234123457 | 7 | 7 | Valid |
| 2 | 036000291452 | 2 | 2 | Valid |
| 3 | 9780306406157 | 7 | 7 | Valid |
| 4 | 5901234123458 | 8 | 7 | Invalid |
| 5 | 96385074 | 4 | 4 | Valid |
| 6 | 10012345678902 | 2 | 2 | Valid |
| 7 | 978030640615 | 5 | 2 | Invalid |
Line 7 is a 12-digit ISBN payload missing its check digit; the validator reads it as a GTIN-12 and fails it.
What is Bulk Checksum Validator?
A Bulk Checksum Validator is a tool that checks hundreds of identifiers in 1 pass and reports which ones fail. The error rate gives a quick measure of data quality.
Truncated values, as on line 7, show up as invalid, which helps find column-cut errors from spreadsheet exports.
What are the uses of Bulk Checksum Validator?
There are 3 main uses of the Bulk Checksum Validator, including catalogue audits, import checks, and data quality reporting.
- Catalogue audits: Retailers check thousands of GTINs at once.
- Import checks: Data teams screen files before loading.
- Data quality reporting: Managers track error rates over time.