The Analysis Types

Analysis Types explains the analyses available for Salesforce objects, where to configure or run them, and how to find and interpret their results in Plauti Monitor.

Active Picklist Value Distribution shows how often each active value on a picklist field is used across records.

Analyze text field lengths, identify statistical outliers, interpret job results, configure thresholds, and apply recommended actions to improve data consistency and prepare fields for automation.

Character Length Distribution groups text field values into length-based segments, and compares average length against the field maximum.

Checkbox Distribution shows the true/false ratio for each checkbox field, helping you spot values that are rarely toggled, or skewed in an unexpected direction.

Categorize date values as too old, within range, or too new, based on a configurable date window. The Date Range Analysis scans selected date and date-time fields, and classifies each value as too

Measure how frequently a field's predefined default value remains unchanged across records. The Default Value Usage analysis type analyzes fields that have a configured default value, such as a

Duplicate Value Rate measures the percentage of duplicate values in text and lookup fields. Duplicate Value Rate measures which percentage of records share their value with at least one other record,

Email Domain Analysis explains how to rank email domains, configure occurrence thresholds, interpret distribution results, and identify provider patterns, concentration, and data quality issues.

Check email field values against a standard format pattern to identify invalid, malformed, or empty email addresses.

The Fill Rate measures data completeness. It calculates the percentage of records that have a non-empty value for a certain field.

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.

Identify records with date values set in the future, to detect potentially incorrect dates. The Future Scans analysis scans date and date-time fields for values that fall after today.

Inactive Picklist Usage reveals records containing deactivated picklist values, helping you assess impact, prioritize cleanup, remap data, and determine when values are safe to delete.

Many-to-One Distribution shows how child records are distributed across parent records in a lookup relationship, helping you spot concentration patterns and parent records with an unusually high

Multi-Select Average Count calculates the average number of values selected per record in a multi-select picklist field.

Negative Value Analysis identifies numeric fields that contain values less than zero. The Negative Value Analysis scans numeric fields such as Number, Currency and Percent fields, and calculates for

Number Analysis calculates key statistics such as minimum, maximum, mean, and standard deviation for numeric fields, and flags values that fall far outside the normal range.

Orphaned Record Detection identifies records where a lookup field is empty, flagging potential broken relationships or missing parent references.

Identify date field values that fall beyond a configured cutoff date in the past. The Past Date Threshold analysis scans date and date-time fields, and flags any value that exceeds a configurable age

Standard Deviation Analysis calculates the mean, standard deviation, and coefficient of variation for numeric fields.

Time Component analysis measures midnight and noon usage in date-time fields, helping you configure thresholds, assess whether time data is meaningful, and guide reporting and AI decisions.

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.

The Unused Active Picklist Values analysis identifies active picklist values that never appear in any record, presenting you with safe candidates for cleanup.

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