Overview
The Standard Checker calculator validates whether your process data meets mindzie's standard process definitions. It verifies that all required attributes and activities are present with correct data types, helping ensure your process data is ready for analysis and that all platform features will work correctly.
This calculator is particularly valuable during data onboarding, ETL validation, and quality assurance workflows to confirm that extracted data meets the structural and content requirements for mindzie's process-specific features.
Common Uses
- Validate data completeness after initial ETL setup or data extraction
- Quality gate checks in automated ETL pipelines to ensure data meets requirements
- Verify that mandatory attributes exist before deploying to production
- Identify missing recommended attributes that would enable additional analysis features
- Assess impact of changes to data extraction logic or source systems
- Troubleshoot why specific calculators or features aren't working as expected
Settings
This calculator has no configurable settings. It automatically validates your data against the appropriate standard based on your process type.
Process Type Detection: The calculator detects your process type from your data and applies the corresponding standard. Supported process types include:
- Procure to Pay
- Accounts Payable
- Accounts Receivable
- Order to Cash
- Service Tickets
Standard Fields:
- Title: Optional custom title for the calculator output
- Description: Optional description for documentation purposes
Examples
Example 1: Validating New Data Extraction
Scenario: You've just completed building your first ETL script for a Procure to Pay process from SAP. Before deploying the dataset to production users, you want to verify that all essential data has been extracted correctly.
Settings:
- Title: "P2P Data Validation - Initial Extract"
- Description: "Quality check before production deployment"
Output:
The calculator displays a summary table showing compliance percentages across three categories:
Category Count Issues Compliance
Mandatory Attributes 42 3 93%
Recommended Attributes 18 8 56%
Derived Attributes 12 0 100%
Below the summary, you'll see detailed issue lists organized by category:
Mandatory Issues (3):
- Purchase Order Amount: Couldn't find case attribute
- Supplier Name: Couldn't find case attribute
- Payment Date: Couldn't find event attribute
Recommended Issues (8):
- Purchase Order Category: Couldn't find case attribute
- Approval Level: Couldn't find case attribute
- (... 6 more attributes)
Insights: The 93% mandatory compliance shows your ETL is capturing most essential data, but three critical attributes are missing. The "Purchase Order Amount" and "Supplier Name" attributes are mandatory because they're required for core financial analysis features. You should update your ETL script to extract these fields before deployment.
The 56% recommended compliance is acceptable for an initial deployment, but adding these attributes would unlock additional analysis capabilities like category-based breakdowns and approval workflow analysis.
Example 2: Post-Upgrade Validation
Scenario: Your source ERP system was recently upgraded, and you want to verify that the data structure hasn't changed in ways that break your mindzie integration.
Settings:
- Title: "Post-Upgrade Validation"
- Description: "Verify data compatibility after ERP upgrade"
Output:
Category Count Issues Compliance
Mandatory Attributes 42 0 100%
Recommended Attributes 18 1 94%
Derived Attributes 12 0 100%
Recommended Issues (1):
- Invoice Currency: Attribute has the wrong type (Expected: String, Found: Number)
Insights: All mandatory attributes are present, which means core functionality is intact. However, one recommended attribute changed from string to numeric type. This likely happened because the ERP upgrade changed how currency codes are stored. While not critical, you should update your ETL to convert the numeric currency codes back to standard three-letter currency strings (USD, EUR, GBP) to match the expected format.
Example 3: Feature Troubleshooting
Scenario: Users report that the "Three-Way Match" calculator isn't working properly in your Accounts Payable process. You suspect missing data attributes.
Settings:
- Title: "AP Three-Way Match Prerequisites"
- Description: "Identify missing attributes for advanced features"
Output:
Category Count Issues Compliance
Mandatory Attributes 38 0 100%
Recommended Attributes 15 0 100%
Derived Attributes 10 4 60%
Derived Issues (4):
- Goods Receipt Value: Couldn't find event attribute (required for three-way matching)
- Purchase Order Line Item Match: Missing required attributes
- Invoice Line Item Match: Missing required activities
- GR-IR Account Balance: Missing required attributes
Insights: The issue is clear: derived attributes needed for three-way matching are missing. These derived attributes depend on other attributes and activities that weren't extracted. Specifically, "Goods Receipt Value" is a key event attribute needed to compare invoice amounts against received goods. Review the "required attributes" referenced in each issue to determine what additional data needs to be extracted from your source system to enable three-way match analysis.
Example 4: Multi-Process Comparison
Scenario: You're setting up both Accounts Payable and Accounts Receivable processes and want to understand how complete each dataset is before presenting to stakeholders.
Settings (Run twice - once for each process):
- Title: "AP Data Completeness"
- Title: "AR Data Completeness"
Output Comparison:
Accounts Payable:
Mandatory: 100% Recommended: 85% Derived: 100%
Accounts Receivable:
Mandatory: 89% Recommended: 45% Derived: 50%
Insights: Your Accounts Payable data is production-ready with excellent coverage across all categories. However, Accounts Receivable has several mandatory attributes missing (89% compliance), which will prevent some core features from working. Before launching AR analysis, focus on extracting the missing mandatory attributes. The lower recommended and derived percentages can be addressed in a phased approach after launch.
Example 5: Automated Quality Gate
Scenario: Your ETL runs nightly, and you want to set up an automated alert if data quality drops below acceptable levels.
Settings:
- Title: "Nightly Data Quality Check"
- Description: "Automated validation in ETL pipeline"
Output:
The calculator provides compliance percentages you can evaluate programmatically:
Mandatory Percent: 0.97 (97%)
Recommended Percent: 0.83 (83%)
Derived Percent: 1.00 (100%)
Insights: You can configure your ETL pipeline to:
- FAIL the job if Mandatory Percent < 0.95 (less than 95%)
- WARN stakeholders if Recommended Percent < 0.70 (less than 70%)
- PASS if all thresholds are met
In this example, the job would pass because mandatory compliance (97%) exceeds your 95% threshold. This approach ensures data quality issues are caught immediately rather than discovered by users.
Output
The calculator produces two primary outputs: a summary statistics table and detailed issue lists.
Summary Statistics Table
Mandatory Count (Number): Total number of mandatory attributes expected for your process type. These attributes are essential for core platform functionality.
Mandatory Issues (Number): Count of mandatory attributes that are either missing or have incorrect data types. Each issue represents a critical data quality problem.
Mandatory Percent (Percentage): Percentage of mandatory attributes that are correctly present. Values are shown as decimals (0.97 = 97%). Aim for 100% before production deployment.
Recommended Count (Number): Total number of recommended attributes defined for your process. These enable advanced features and richer analysis.
Recommended Issues (Number): Count of recommended attributes that are missing or incorrect. Lower priority than mandatory issues but still important for full feature access.
Recommended Percent (Percentage): Percentage of recommended attributes that are correctly present. Values above 80% indicate good data coverage.
Derived Count (Number): Total number of derived attributes that can be calculated from other attributes. These are typically calculated fields or computed metrics.
Derived Issues (Number): Count of derived attributes that cannot be calculated due to missing dependencies (required attributes or activities).
Derived Percent (Percentage): Percentage of derived attributes that can be successfully calculated. Issues here often indicate gaps in foundational data.
System Name Version (Text): The name and version of your source system extracted from the event data. Useful for tracking which ERP or system version the data came from.
Extraction Version (Text): The version of your ETL or data extraction process. Helps track which extraction logic was used.
Process Name (Text): The identified process type (for example, "Procure to Pay" or "Accounts Payable").
Detailed Issue Lists
The calculator displays separate lists for each category of issues:
Mandatory Attribute Issues: Lists mandatory attributes that are missing or have incorrect data types. Each issue includes:
- Attribute name
- Problem description (for example, "Couldn't find case attribute" or "Attribute has the wrong type")
- Expected data type versus actual data type (for type mismatches)
Recommended Attribute Issues: Similar to mandatory issues but for recommended attributes. These are lower priority but should be addressed to unlock additional features.
Derived Attribute Issues: Lists derived attributes that cannot be calculated. Issues may include:
- Missing required base attributes
- Missing required activities
- Configuration problems with dependency definitions
Activity Issues: Lists standard activities that are expected but not found in your event log. Standard activities are predefined events like "Create Purchase Order" or "Approve Invoice" that enable process-specific analysis.
Interpretation Guidance:
- 100% Mandatory compliance: Data is production-ready for core features
- 95-99% Mandatory compliance: Minor gaps, review and address before deployment
- Below 95% Mandatory compliance: Significant gaps, ETL work required
- Above 80% Recommended compliance: Excellent data coverage
- 50-80% Recommended compliance: Good, consider enhancing over time
- Below 50% Recommended compliance: Limited feature access, prioritize improvements
This documentation is part of the mindzie Studio process mining platform.