compare multiple case attributes

Overview

The Compare Multiple Case Attributes enrichment extends basic comparison capabilities by validating that multiple case attributes all contain identical values. This powerful data quality operator creates a boolean result indicating whether all selected attributes match across a case, enabling comprehensive validation scenarios where consistency across multiple data points is critical. Unlike simple two-attribute comparisons, this enrichment checks for universal agreement among three or more attributes, providing essential capabilities for complex data validation, multi-system reconciliation, and comprehensive quality assurance.

This enrichment is particularly valuable in process mining scenarios involving data from multiple source systems, redundant data entry points, or complex validation requirements. For example, in three-way matching scenarios common in procurement, you can verify that quantity values match across purchase orders, goods receipts, and invoices. In healthcare settings, you can validate that patient identifiers are consistent across admission, treatment, and discharge records. In manufacturing, you can ensure that product specifications align across design, production planning, and quality control systems. The enrichment returns True only when all specified attributes contain exactly the same value, making it ideal for detecting any inconsistency among multiple related data points.

The comparison algorithm processes attributes sequentially, comparing each subsequent attribute against the first one. If any attribute differs from the first attribute's value, the result is False. If any attribute contains a null value, the result is null (not calculated), ensuring that incomplete data doesn't produce misleading validation results. This approach provides a robust foundation for data quality monitoring and process conformance checking across complex multi-system environments.

Common Uses

Validate three-way matching in procurement processes by ensuring purchase order, goods receipt, and invoice quantities all match
Verify patient identifiers remain consistent across admission, treatment, billing, and discharge systems in healthcare
Ensure product specifications match across design documents, production orders, and quality inspection records
Validate that customer information is synchronized across CRM, order management, and billing systems
Check that approval amounts align across requisition, approval workflow, and payment authorization systems
Verify shipping quantities match across warehouse management, transportation, and customs documentation
Ensure compliance by validating that audit trail timestamps match across multiple logging systems

Settings

New Attribute Name: Specify the name for the boolean attribute that will store the comparison result. Choose a descriptive name that clearly indicates what multi-attribute validation is being performed. For example, "Three_Way_Match_Quantity" when comparing purchase order, goods receipt, and invoice quantities, or "Patient_ID_Consistent" when validating patient identifiers across multiple systems. The attribute will contain True when all compared values match, False when any value differs, and null when any attribute contains a null value.

Case Column Names: Select the case attributes to compare for equality. This multi-select field allows you to choose three or more attributes from all available case attributes in your dataset, including both original attributes and those created by other enrichments. The attributes can be of any data type - text, numeric, date, or boolean. The enrichment validates that all selected attributes contain identical values for each case. A minimum of two attributes must be selected, but the enrichment is designed for scenarios with three or more attributes. The comparison checks for exact equality - all values must be precisely the same, including data type and format. If any attribute in the list contains a null value, the comparison result is null rather than True or False, ensuring that incomplete data is properly flagged for investigation.

Examples

Example 1: Three-Way Match in Procurement

Scenario: In a procure-to-pay process, you need to validate that the quantity values match across three critical documents - the purchase order, goods receipt, and invoice - before authorizing payment. This three-way match is a fundamental control for financial accuracy and fraud prevention.

Settings:

New Attribute Name: Three_Way_Match_Quantity
Case Column Names: PO_Quantity, GR_Quantity, Invoice_Quantity

Output: Creates a boolean attribute "Three_Way_Match_Quantity" with values:

True: When all three quantities match exactly (e.g., PO=100, GR=100, Invoice=100)
False: When any quantity differs (e.g., PO=100, GR=100, Invoice=105)
null: When any of the three quantity fields is missing or null

Sample data showing different scenarios:

Case_ID	PO_Quantity	GR_Quantity	Invoice_Quantity	Three_Way_Match_Quantity
PO-001	100	100	100	True
PO-002	50	50	52	False
PO-003	200	195	200	False
PO-004	75	null	75	null
PO-005	25	25	25	True

Insights: This comparison enables automatic approval of invoices with perfect three-way matches while flagging discrepancies for manual review. Organizations can calculate three-way match rates as a KPI for process efficiency, identify suppliers with frequent discrepancies, and measure the financial impact of mismatches. Cases with False results require investigation, while null results indicate incomplete data requiring data quality improvement.

Example 2: Patient Identifier Validation in Healthcare

Scenario: In a hospital information system, patient identifiers must remain consistent across the admission system (ADT), electronic medical records (EMR), laboratory information system (LIS), and billing system. Inconsistent identifiers can lead to medical errors, billing problems, and regulatory compliance issues.

Settings:

New Attribute Name: Patient_ID_Consistent
Case Column Names: ADT_Patient_ID, EMR_Patient_ID, LIS_Patient_ID, Billing_Patient_ID

Output: Creates a boolean attribute "Patient_ID_Consistent" indicating:

True: When all four system identifiers match (e.g., all show "PT-789456")
False: When any system has a different identifier, indicating a data synchronization issue
null: When any system has missing identifier information

Sample data:

Case_ID	ADT_Patient_ID	EMR_Patient_ID	LIS_Patient_ID	Billing_Patient_ID	Patient_ID_Consistent
ADM-101	PT-789456	PT-789456	PT-789456	PT-789456	True
ADM-102	PT-445821	PT-445821	PT-445821	PT-445281	False
ADM-103	PT-223344	PT-223344	null	PT-223344	null
ADM-104	PT-998877	PT-998877	PT-998877	PT-998877	True

Insights: This validation helps identify master data management issues requiring immediate attention, as inconsistent patient identifiers can lead to serious medical errors. Healthcare organizations can track the percentage of cases with consistent identifiers across systems, prioritize system integration improvements, and ensure regulatory compliance for patient data management. False results trigger data reconciliation workflows, while null results indicate incomplete registration processes.

Example 3: Product Specification Consistency in Manufacturing

Scenario: In a manufacturing environment, product specifications must align across engineering design documents, production planning systems, and quality control databases to ensure products meet requirements. Inconsistencies can result in production of non-conforming products or unnecessary production delays.

Settings:

New Attribute Name: Spec_Consistent_All_Systems
Case Column Names: Design_Material_Grade, Planning_Material_Grade, QC_Required_Grade

Output: Creates a boolean attribute "Spec_Consistent_All_Systems" showing:

True: When all three systems specify the same material grade (e.g., all specify "Grade_A_Premium")
False: When any system has different specifications (e.g., Design specifies "Grade_A_Premium" but Planning shows "Grade_A_Standard")
null: When specification data is missing from any system

Sample data:

Production_Order	Design_Material_Grade	Planning_Material_Grade	QC_Required_Grade	Spec_Consistent_All_Systems
WO-5001	Grade_A_Premium	Grade_A_Premium	Grade_A_Premium	True
WO-5002	Grade_B_Standard	Grade_B_Standard	Grade_A_Premium	False
WO-5003	Grade_C_Economic	null	Grade_C_Economic	null
WO-5004	Grade_A_Premium	Grade_A_Premium	Grade_A_Premium	True

Insights: This comparison enables early detection of specification inconsistencies before production begins, preventing quality issues and material waste. Manufacturing organizations can measure the rate of specification alignment across systems, identify specific products or product families with frequent inconsistencies, and improve engineering change management processes. False results trigger specification review workflows to resolve conflicts before production starts.

Example 4: Customer Data Synchronization Across Systems

Scenario: In an enterprise with multiple customer-facing systems, customer email addresses must be synchronized across the CRM system, e-commerce platform, email marketing system, and customer service portal to ensure consistent communication and accurate customer records.

Settings:

New Attribute Name: Customer_Email_Synchronized
Case Column Names: CRM_Email, Ecommerce_Email, Marketing_Email, Support_Email

Output: Creates a boolean attribute "Customer_Email_Synchronized" with:

True: When all systems have the same email address (e.g., all show "customer@example.com")
False: When email addresses differ across systems, indicating synchronization issues
null: When email address is missing from any system

Sample data:

Customer_ID	CRM_Email	Ecommerce_Email	Marketing_Email	Support_Email	Customer_Email_Synchronized
CUST-1001	john@example.com	john@example.com	john@example.com	john@example.com	True
CUST-1002	mary@company.com	mary@company.com	mary@oldmail.com	mary@company.com	False
CUST-1003	bob@business.net	bob@business.net	null	bob@business.net	null
CUST-1004	lisa@enterprise.io	lisa@enterprise.io	lisa@enterprise.io	lisa@enterprise.io	True

Insights: This validation helps identify customers with inconsistent contact information who may miss important communications or receive duplicate messages. Organizations can calculate data synchronization rates across systems, prioritize master data management improvements, and reduce customer service issues caused by outdated contact information. False results trigger data synchronization workflows, while null results indicate incomplete customer profiles.

Example 5: Financial Approval Amounts Alignment

Scenario: In a purchase requisition and approval process, the requested amount must remain consistent as it flows through multiple approval levels and systems to prevent unauthorized amount changes and ensure financial controls are functioning correctly.

Settings:

New Attribute Name: Approval_Amounts_Aligned
Case Column Names: Requisition_Amount, L1_Approval_Amount, L2_Approval_Amount, PO_Final_Amount

Output: Creates a boolean attribute "Approval_Amounts_Aligned" indicating:

True: When all approval levels show the same amount (e.g., all show 15,000.00)
False: When amounts differ across approval stages, indicating unauthorized changes
null: When amount data is missing from any stage

Sample data:

Requisition_ID	Requisition_Amount	L1_Approval_Amount	L2_Approval_Amount	PO_Final_Amount	Approval_Amounts_Aligned
REQ-2001	15000.00	15000.00	15000.00	15000.00	True
REQ-2002	8500.00	8500.00	8750.00	8750.00	False
REQ-2003	22000.00	22000.00	null	22000.00	null
REQ-2004	5000.00	5000.00	5000.00	5200.00	False

Insights: This comparison ensures financial control integrity by detecting unauthorized amount changes during the approval workflow. Organizations can identify cases where amounts were modified without proper authorization, investigate approval process compliance, and strengthen financial controls. False results trigger immediate investigation for potential fraud or process violations, while a high rate of True results confirms that financial controls are functioning as designed.

Output

The Compare Multiple Case Attributes enrichment creates a single new boolean case attribute with the name specified in the settings. This attribute contains True when all compared attributes have identical values, False when any attribute differs from the others, and null when any attribute contains a null value. The comparison is performed for each case independently.

The enrichment uses a sequential comparison algorithm that compares the first attribute against each subsequent attribute. All values must match exactly, including data type and format. The result is:

True: All selected attributes contain identical non-null values
False: At least one attribute has a different value (but all compared attributes are non-null)
null: One or more attributes contain null values, indicating incomplete data

The boolean attribute can be displayed in different formats depending on your visualization preferences - as True/False, Yes/No, 1/0, or with custom labels. This attribute integrates seamlessly with other mindzieStudio features:

Filtering: Filter cases to show only complete matches (True), any mismatches (False), or incomplete data (null)
Conformance Analysis: Calculate the percentage of cases with perfect multi-attribute alignment versus those with discrepancies
Process Flows: Create different process paths based on whether all attributes match
Calculators: Use in logical expressions for complex validation rules, such as "(Three_Way_Match_Quantity = True) AND (Amount < 10000)"
Dashboards: Create KPIs showing match rates, trend analysis of data quality over time, and identify systems with frequent inconsistencies
Data Quality Monitoring: Track null results to identify data completeness issues requiring investigation

The enrichment is particularly effective when combined with other comparison enrichments to build comprehensive validation hierarchies. For example, you might first use Compare Multiple Case Attributes to check if three quantity fields match, then use a separate comparison to validate that matched quantities also meet a threshold requirement.

Overview

Common Uses

Settings

Examples

Example 1: Three-Way Match in Procurement

Example 2: Patient Identifier Validation in Healthcare

Example 3: Product Specification Consistency in Manufacturing

Example 4: Customer Data Synchronization Across Systems

Example 5: Financial Approval Amounts Alignment

Output

See Also