Uniqueness Detection

Plauti Context Analysis Types

Last published at: May 12th, 2026

Uniqueness Detection checks whether all values in a field are unique across all records. 

 

Uniqueness Detection checks whether all non-blank values in a field occur only once across all records. 

Use to scan fields that could act as key identifier fields, such as text, number, email or codes. Find suitable candidates for keys, or fields that could benefit from a unique constraint.

This analysis does not return a percentage of unique fields or values. For duplicate frequency, use the Duplicate Value Rate analysis.

Configuration

Indicate whether the check for uniqueness should be case sensitive or not. When Case Sensitive is enabled, values aa and Aa will be considered two separate, unique values.

Detailed Job Results

The summary shows how many of the analyzed fields have all-unique values. In the ‘Unique Fields’ section, all unique fields of the Object are listed. Below, in the 'Field Details' section, "Is Unique" again indicates which fields only have unique (or blank) values across all records. In addition, "Has Unique Property" shows whether the field has a unique constraint defined in its Salesforce field settings.

Key Insights

  • Possible ID candidates: Fields where all non-blank values are unique are strong candidates to function as a key field for External ID, upserts, integrations, and matching
  • ID-looking fields that aren't unique: Fields that appear to be IDs but turn out not to be unique (e.g. reused ticket numbers) are not safe for matching or upsert logic
  • Blanks vs. collisions: Fields with all unique filled values but frequent blanks can still be good key candidates; however true collisions (duplicate field values present) make fields unusable as key fields
Scenario Actions
Unique = TRUE, use field as key ID field
  • Confirm that the field is stable and owned by a clear system or process
  • Mark the field as ‘External ID’ in Salesforce, and as ‘Unique’ if needed
  • Use it for upsert operations and deterministic matching
  • Document in your data dictionary
Unique = FALSE (avoid as key ID field)
  • Do not use as External ID or primary match key
  • If the field should be unique, use the Duplicate Value Rate analysis to assess severity, clean up data, and add validation rules/flows to enforce uniqueness
Designing unique keys and integrations
  • Promote fields where Unique = TRUE to External ID or primary business key
  • Create new synthetic key (e.g. auto-number) if none of the existing fields is unique
  • Prefer unique fields over names or emails in integrations
Preparing for matching/deduplication/AI
  • Prioritize Unique = TRUE fields as exact match keys
  • Use Unique = FALSE fields only as supporting attributes in fuzzy matching
  • Combine with the Duplicate Value Rate analysis to monitor and reduce duplicate values