Overview
The Boolean Counts calculator analyzes all boolean (true/false) attributes in your process data and displays how many cases have true values for each attribute. It shows both the count and percentage of cases where each boolean flag is set to true, helping you understand the prevalence of binary characteristics across your process.
This calculator automatically identifies all boolean columns in your case data and provides a comprehensive summary, making it easy to spot patterns in compliance flags, quality indicators, process completion markers, or any other yes/no characteristics.
Common Uses
- Track compliance rates across multiple regulatory requirements
- Analyze feature adoption or option selection rates
- Monitor quality check pass rates across different inspection types
- Measure process completeness indicators (e.g., document received, approval obtained)
- Identify which boolean flags are most commonly true or false
- Compare prevalence of different binary characteristics in your process
Settings
Attribute Names (Optional): Select specific boolean attributes to analyze. If left empty, the calculator will automatically analyze all boolean attributes in your case data.
Leave this setting empty to get a complete overview of all boolean flags, or select specific attributes when you want to focus on particular characteristics.
Examples
Example 1: Compliance Requirements Analysis
Scenario: Your procurement process has multiple compliance requirements tracked as boolean flags (e.g., ContractSigned, BudgetApproved, SecurityReviewed, ManagerApproved). You want to see which requirements are most commonly met and identify potential compliance gaps.
Settings:
- Attribute Names: (leave empty to analyze all boolean attributes)
Output:
The calculator displays a table with one row per boolean attribute:
- ContractSigned: 847 cases (94.2%)
- BudgetApproved: 782 cases (87.0%)
- SecurityReviewed: 623 cases (69.3%)
- ManagerApproved: 899 cases (100.0%)
Insights: Manager approval is consistently obtained (100%), but security reviews are only completed in 69% of cases, revealing a potential compliance gap. This suggests you may need to strengthen the security review process or investigate why it's being skipped in nearly one-third of procurement cases.
Example 2: Quality Inspection Analysis
Scenario: Your manufacturing process tracks multiple quality checks as boolean flags (e.g., DimensionsPass, MaterialsPass, FinishPass, FunctionalPass). You want to identify which quality checks have the highest failure rates.
Settings:
- Attribute Names: DimensionsPass, MaterialsPass, FinishPass, FunctionalPass
Output:
The calculator shows pass rates for each quality check:
- DimensionsPass: 1,234 cases (98.7%)
- MaterialsPass: 1,189 cases (95.1%)
- FinishPass: 1,098 cases (87.8%)
- FunctionalPass: 1,242 cases (99.4%)
Insights: The finish quality check has the lowest pass rate at 87.8%, indicating this is where most quality issues occur. You should investigate the finishing process to understand why nearly 12% of items fail this inspection and implement improvements to reduce defects.
Example 3: Document Completeness Dashboard
Scenario: Your loan application process requires various documents (e.g., IncomeProofReceived, IdentityVerified, CreditCheckCompleted, EmploymentConfirmed). You want to create a dashboard showing document collection rates.
Settings:
- Attribute Names: (leave empty to see all document flags)
Output:
The calculator displays collection rates for each document type:
- IncomeProofReceived: 456 cases (91.2%)
- IdentityVerified: 498 cases (99.6%)
- CreditCheckCompleted: 482 cases (96.4%)
- EmploymentConfirmed: 423 cases (84.6%)
Insights: Employment confirmation has the lowest completion rate at 84.6%, creating a bottleneck in the loan approval process. This suggests you may need to improve the employment verification process or provide better communication to applicants about this requirement. The high identity verification rate (99.6%) shows this step is working effectively.
Output
The calculator produces a table with the following columns:
- Attribute Name: The display name of each boolean attribute
- Case Count: The number of cases where the attribute has a true value
- Percentage: The percentage of total cases where the attribute is true
Each row represents one boolean attribute, making it easy to compare prevalence rates across different characteristics. The results are sorted alphabetically by attribute name.
You can add this analysis to a dashboard to monitor boolean characteristics over time or use it as a starting point for deeper investigation by clicking on specific rows to see which cases have particular flags set.
This documentation is part of the mindzie Studio process mining platform.