Format Pattern Detection

Plauti Context Analysis Types

Last published at: May 12th, 2026

Format Pattern Detection identifies structural patterns in text values, and shows how much of each field's data is covered by the top 5 detected patterns. 

 

Format Pattern Detection identifies formatting patterns in text fields by analyzing the structure of field values. It groups the different patterns it finds above a minimum occurrence threshold.

Use for analyzing fields that should follow a structural pattern (e.g. Email, Phone, ID fields, Codes, custom text). Find out whether the values of a field have a consistent, standard format or a mix of incompatible formats.

Configuration

Set a minimum occurrence for a pattern to be displayed as a pattern group in the analysis results. This will leave out patterns that only occur a few times and not consistently.

Detailed Job Results

In the analysis results, letters are replaced with "X", digits with "0", and special characters are preserved. For example "555-123-4567" is counted in the result group “000-000-0000”. Empty fields are not taken into account.

The doughnut chart shows how much of each field's data is covered by the different detected patterns. In the table below, each pattern percentage is relative to records that have a value, excluding blanks.

Key Insights

  • Format consistency: One or few patterns covering most records means that a de facto standard is in use, while many small patterns points to inconsistent entry styles
  • Implicit standards: Identify dominant patterns already used by teams
  • Invalid formats: Patterns like XX, 0, X, or free-text (XXXXXXXXX) reveal bad data, incomplete entries, or field misuse
  • Integration & regional behavior: Spot differences between user-entered vs. integration-written data, or regional format variations
Scenario Actions
One or few dominant patterns
  • Document expected patterns in field help text
  • Add validation rules/regex/input masks using the pattern
  • Normalize legacy data to your chosen standard (e.g. convert 0000XX to 0000 XX)
Many inconsistent patterns
  • Group similar pattern variants
  • Define one or two formats per use case/region
  • Batch-clean existing values into the selected standard
  • Set up validation and UI masks on forms, and standardize integration mappings, to output the selected standard
Invalid/placeholder formats (e.g. X, 0, XXXX)
  • Mark as invalid
  • Block new saves via validation
  • Normalize historical data (convert to empty, or trigger enrichment for critical records)
Using patterns in reporting and AI
  • Create derived fields: Format_Pattern__c (stores pattern label), Is_Standard_Format__c (TRUE if approved pattern), Is_Format_Anomaly__c (TRUE if rare/non-standard)
  • Use derived fields for quality dashboards, routing, scoring, and anomaly detection