By Team Hypomnema · Data
Compare approximate record names and review ranked match candidates.
Use the guide alongside the working tool.
Open Fuzzy Record Matcher →Explicit working sample
id,company
1,Northwind Studio
2,Acme Design
3,Blue River CoCompare approximate record names and review ranked match candidates. Suggestions use normalized edit distance or token Jaccard similarity, not identity verification. Each input row gets its top candidates without automatic merging. Ties are retained deterministically. Limited comparison work and 200-character normalized keys keep processing bounded; reduce the datasets when requested.
Prepare the input
Compare approximate record names and review ranked match candidates.
CSV, TSV and arrays of scalar JSON objects are supported. Identifiers stay text. Empty cells are missing by default; add special missing tokens only if their meaning is certain. Use the sample to inspect the exact workflow.
Review the method
Suggestions use normalized edit distance or token Jaccard similarity, not identity verification. Each input row gets its top candidates without automatic merging. Ties are retained deterministically. Limited comparison work and 200-character normalized keys keep processing bounded; reduce the datasets when requested.
Keep the useful output
Review the summary and result records. Transformation tools retain separate change, rejected-record or diagnostics datasets where relevant. Choose the dataset before exporting; a report and a cleaned dataset serve different purposes.
CSV exports protect formula-like text. JSON keeps data portable without making an Excel workbook. Reports and editable backups may contain private source values; download only what you intend to share. No automatic account storage or server processing is used.
Ready to apply this workflow?
Try Fuzzy Record Matcher →Explore a related workflow
- Data Quality Profiler: methods and data limits
Inspect missingness, uniqueness, inferred types and numeric summaries.
- Missing Value Cleaner: methods and data limits
Fill or remove missing values with a complete, reviewable change log.
- Outlier Detector: methods and data limits
Flag unusual numeric values using IQR fences, z-scores or median deviation.
- Data Validation Rules: methods and data limits
Check records against clear rules and separate passing and failing rows.
Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.