Cleaning spreadsheet data before document generation

Dirty data produces confident-looking wrong documents

Document generation renders whatever it is given, at scale. A trailing space in a name column produces a certificate with a subtly wrong name; an ambiguous date column produces offer letters with the wrong joining month. Because the output looks polished, errors are harder to spot than in the raw spreadsheet.

The five problems that break batches most often

• •Duplicate rows — usually from combining exports. Deduplicate before import, or two identical documents (with a filename suffix) will be generated.
• •Mixed date formats — 03/04/2026 means different things to different tools. DocForge parses day-first when unambiguous, but normalizing to YYYY-MM-DD removes all doubt.
• •Numbers stored as text with currency symbols and grouping — ₹1,45,000 in a cell is parsed tolerantly, but a plain 145000 is safer.
• •Invisible whitespace and non-breaking spaces from copy-pasting — they survive into rendered documents.
• •Encoding damage (names showing � characters) from files saved in legacy encodings; re-export the file as UTF-8.

Use a data-quality pass first

For messy exports, run the file through a data-preparation tool before the merge. Motifuse's Reconova profiles CSV and Excel data locally in your browser: it scores completeness and validity, finds duplicates, and lets you build a reviewable cleaning pipeline. The cleaned export then imports into DocForge with far fewer validation failures.

Let validation be the safety net, not the strategy

DocForge validates every row before generation — that is the last line of defense, not the plan. Fixing the source spreadsheet fixes every future batch; excluding failed rows fixes only today's.