The Analysis Types
Learn how Analysis Types assess field completeness, uniqueness, validity, patterns, dates, defaults, and value distributions to identify data quality issues and improvement opportunities.
Email Regex Validation
Email Regex Validation checks email fields against a standard format, categorizes values as valid, invalid, or empty, and helps assess data quality, deliverability risk, and cleanup priorities.
Time Component Analysis
Analyze date-time fields for midnight versus non-midnight values, configure thresholds, interpret results, and assess whether time data supports reliable reporting, SLA logic, and AI use.
Date Range Analysis
Analyze date and date-time fields against configurable fixed or relative windows to identify stale and future records, review field-level results, and improve data quality.
Past Date Threshold
Configure fixed or relative cutoff dates to identify stale or anomalous date values, interpret field-level results, and guide data-quality and lifecycle improvements.
Future Date Analysis
Future Date Analysis identifies date values after today, groups anomalies by range, and helps configure thresholds and address data errors affecting reporting, SLAs, and automation.
Default Value Usage
Measure how often fields retain predefined defaults, interpret passive and active usage, configure thresholds, and identify fields suitable for reporting, process logic, and AI models.
Duplicate Value Rate
Duplicate Value Rate explains how to measure and interpret repeated field values, configure case sensitivity and thresholds, and identify data quality issues requiring deduplication or validation.
Uniqueness Detection
Identify fields with unique non-blank values, configure case sensitivity, interpret results, and select reliable key fields for integrations, matching, and upserts.
Format Pattern Detection
Analyze text fields for recurring format patterns, measure top-five coverage, identify inconsistent or invalid values, and define validation, normalization, and reporting actions.
Standard Deviation
Standard Deviation analysis measures numeric field variability using the mean, standard deviation, and coefficient of variation to identify outliers, data quality issues, and validation needs.
Value Distribution
Analyze field value frequencies, identify dominant or suspicious entries, and uncover standardization opportunities using configurable thresholds and detailed results.
Fill Rate
Learn how Fill Rate measures field completeness, configure thresholds, interpret job results, and identify fields to improve, enforce, monitor, or retire for reliable reporting and automation.
Analysis Types
Learn how Analysis Types evaluate Salesforce Objects, configure them for jobs, add them to Ad Hoc Context Jobs, and review results in the Context Library and job results.