Date Range Analysis

Plauti Context Analysis Types

Last published at: June 11th, 2026

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 old, within range, or too new, against a configurable start and end date window. Define the relevant time window per object and field, for example "only dates within the last two years" or "only dates between go-live and today." Results are aggregated per field to surface date-range related issues at a glance. 

Use for identifying stale records, finding records that fall outside expected time boundaries, validating data entry quality, and ensuring that reporting, forecasting, and AI models operate on relevant, current data. 

For checking whether individual dates are simply too far in the past or set in the future, rather than testing against a defined window, consider using Past Date Threshold or Future Date instead.

Configuration

Decide to use fixed dates, that stay the same each time the analysis is used, or relative date literals that are recalculated at each analysis run in relation to today's date. 

Then, enter a From Date and a To Date for the date range to analyze. For fixed dates, these From and To dates themselves are also considered ‘within range’. All older and newer dates are counted as too old or too new. For date literals it depends on the date literal used whether the date itself is included or not.

  • For Fixed Date, type dates or select them with the Calendar  button. 
  • For Date Literal, enter relative dates. For example, use LAST_N_DAYS:100 and NEXT_N_DAYS:100 to find date values between a hundred days ago and a hundred days in the future.  
    Each time you use the analysis with this date literal, the threshold date is recalculated. 
    Read more about how to format Date Literals here.

Detailed Job Results

The doughnut charts show for each field the distribution of records that are too old (red), within your range (green), or too new (amber). Hover over each section to view the number of records, and percentage of the total records with a value.

The results table shows for each field 

  • the number of records with a value, 
  • the configured From Date and To Date, 
  • the anomaly records: the total number of records that are either too old or too new, 
  • the anomaly percentage: the anomaly records as a percentage of all records with a value,
  • the percentage Too Old: the records that are too old as a percentage of all records with a value,
  • the percentage Too New: the records that are too new as a percentage of all records with a value.

Key Insights

  • Data relevance: See how much of your data falls within the time window that matters for your process.
  • Historical noise: A high ‘Too Old’ percentage signals legacy or stale records that may clutter reports and skew AI models.
  • Process health: Records that should have been updated or closed but remain outside the expected time window indicate lifecycle or data entry issues.
     
Scenario Actions
High Out of Range percentage
  • Review records to find the reason for the high rate.
  • Add validation rules or flow checks to block out-of-range input.
  • Fix forms, quick actions, and integrations that set out-of-range dates.
  • Clean existing data, for example with Plauti Manipulate.
High Too Old percentage
  • Implement auto-close or auto-archive rules to handle records that exceed lifecycle expectations.
  • Improve user guidance and record ownership assignment.
Protecting reports and process logic
  • Exclude or flag out-of-range records in operational reports and SLA dashboards.
  • Use date range results to define a trusted data window for reporting.
AI scoring or model training
  • Define an acceptable date window per field for model input.
  • Restrict model input to records within that window.