The Fill Rate measures data completeness by calculating the percentage of records that have a non-empty value for each field. A fill rate of 100% means all analyzed records have a value filled in for that field; 0% means the field is empty on all records. 

Use for identifying data completeness issues, and evaluating field readiness for reporting and automation. Decide which fields to improve completion for, or deprecate.

Configuration

Set a threshold for what constitutes a good, warning level, or critical fill rate. For most fields that you apply a Fill Rate analysis to you'll want to have as much fields filled as possible. This would mean for example a good fill rate is 90% or more, warning level would be between 80-90%, and anything less than 80% filled would be critical. 

For those cases where having empty fields is good, you can reverse the threshold signalling by clicking the Reverse button. 

Detailed Job Results

In the Context Job Results, the doughnut chart groups all analyzed fields into fill rate ranges (0-20%, 20-40%, etc.) to show overall data completeness at a glance. Hover over the different ranges to see the number of records in that range. 
The bar chart sorts the fields by their fill rate; the fields with the number of records containing a value are listed at the top. 
In the Field Details table, "Fill Rate" is the percentage of records that have a non-empty value, and "# Records Without Value" shows the absolute count of empty records per field.

Key Insights

• Data completeness: which fields are well-populated vs. mostly empty
• Reality check on ‘Required’ fields: shows whether they’re really filled in practice
• Trust level for analytics/AI: which fields are reliable for use in reports and AI models
• Field usage patterns: 
  • High Fill Rate = actively used, important in process
  • Very low Fill Rate = candidate for clean-up or removal
• Trend monitoring: Are key fields silently degrading? Is completeness improving after a change? 

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