Value Distribution

Plauti Context Analysis Types

Last published at: May 11th, 2026

Value Distribution shows the frequency of distinct values in a field, revealing which values dominate and how spread out the data is. 

 

Value Distribution shows the distribution of values for selected fields, displaying those values that meet the configured minimum occurrence threshold. By revealing which values dominate and how spread out the data is, this analysis helps identifying data patterns, anomalies, and potential standardization opportunities.  

The Value Distribution analysis uses a minimum occurrence threshold; values that appear less often than the threshold are not displayed in the results. This way only oft-occurring values are presented for review.

Value Distribution or Duplicate Value Rate?

Value Distribution focuses on which values dominate, and how skewed the distribution is. To analyze how many records are in duplicate value groups, use the Duplicate Value Rate analysis type instead.

 

Configuration

Set a minimum occurrence threshold. Values that are used less often than the threshold are not displayed in the results.

Detailed Job Results

Each field section displays a table of the top 20 most frequently occurring values, sorted by count. The "Percentage" column shows how large a share of total records each value represents. Blank or null values are displayed as "(blank)".

Key Insights

  • Value concentration: Shows whether data is well-spread or concentrated on a few values
  • Suspicious overrepresented values: Highlights placeholders (e.g. [not provided]), test data, default dates (e.g. 01/01/1970), old system defaults, and dummy values (e.g.,test@example.com)
  • Field design issues:
    • Few dominant values = users overusing generic options like Other
    • Many single-occurrence values = field too free-form, causing inconsistent data entry
  • Analytics readiness: Determines if a field is informative enough for segmentation, AI models, or reporting
Scenario Actions
Many placeholder/test/default values
  • Decide whether the values should be replaced with a valid value, cleared (blanked) so it is clearly “missing”, or mapped to a better category (e.g. more specific reason codes).
  • Adjust validation rules, flows, and integration mappings to prevent new records from using those values. Enforce better defaults or require a real value.
Overrepresented business values
  • Investigate why few values dominate. It can be a real business signal or a data entry / process issue. Are users defaulting to a single option because others are unclear? Did an integration configure one hard‑coded value?
  • Refine picklist options and help texts
  • Adjust integration mappings
Many one-off values
  • Identify synonyms and variants
  • Convert to controlled picklists
  • Add normalization rules
  • Create standardized fields for reporting
Preparing for reporting/routing/AI
  • Check distribution before using field in critical logic
  • Exclude or down-weight heavily skewed fields
  • Document field health in data dictionary