Recruitment CRM Data Cleanup
Consolidate contact exports without hiding conflicting information.
The problem.
Repeated contacts and inconsistent field names make a CRM export difficult to use.
Illustrative business scenario only. No real organisation, client result or confidential record is represented.
The process.
Map contact fields, trim whitespace and normalise email casing. Merge exact duplicate records only; conflicting records and shared names remain separate.
Synthetic input data
Inspect the source records and download the demonstration assets in the interactive workspace below. Transformations run locally in your browser.
The result.
Use the filters, inspect the records and compare the calculated outputs.
Explicit source-field mapping
Two synthetic exports map into one contact schema. Export A: candidate_id → id, full_name → name, email_address → email, telephone → phone, job_title → role. Export B: contactId → id, displayName → name, email → email, phone → phone, position → role. Only listed mappings are applied; blank fields stay blank. The comparison below shows the consolidated mapped input and the cleaned output.
Data-quality notes & limitations.
Matching names do not establish identity. Email conflicts require review; no missing telephone number or employer is inferred.
Findings are calculated from the selected synthetic records in the workspace above. They describe these inputs only and do not predict business outcomes. A real engagement requires agreed definitions, source checks and a factual review.
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