Analysis

Check Spreadsheet Data Quality Before Matching

Measure blanks, outer whitespace, repeated values and case inconsistencies that can interfere with matching and reporting.

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What this tool does

Small cleanliness problems can create large matching failures. The Data Quality Checker reviews each column for blank cells, values with unwanted outer whitespace, repeated normalized values and capitalization variants that may represent inconsistent entry. It presents these as issue signals rather than automatically declaring each occurrence an error: duplicate department names are normal, while duplicate invoice numbers may be critical. The purpose is to focus a reviewer’s attention before a lookup, import or reconciliation begins.

How to use it

  1. Upload the source dataset.
  2. Review issue signals by column.
  3. Decide which signals are valid repetition and which require cleanup before matching.

Limitations and review points

The checker does not know your organisation’s business constraints. Repeated values and blank optional fields may be legitimate, so all findings require contextual review.

Frequently asked questions

Does an issue signal always mean an error?

No. The report highlights patterns that deserve review, not automatic failures.

Are leading and trailing spaces counted?

Yes. Outer whitespace is measured from the raw cell text.

Does the tool change the file?

No. It only reports findings.

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