hide attribute

Overview

The Hide Attribute enrichment provides a powerful way to simplify your dataset views by selectively hiding attributes from display without permanently deleting the underlying data. This cleanup operator removes specified attributes from the visible dataset, making it easier to focus on relevant information while maintaining data integrity. Unlike deletion operations that permanently remove data, hiding attributes is a non-destructive operation that simply removes columns from the user interface while preserving the original data structure.

This enrichment is particularly valuable when working with datasets containing numerous attributes that may clutter the view or distract from core analysis objectives. By hiding irrelevant, temporary, or intermediate calculation attributes, you can create cleaner, more focused datasets that highlight the most important process information. The Hide Attribute enrichment respects system constraints and prevents hiding of mandatory columns such as Case ID, Activity, and Timestamp, ensuring the dataset remains functional for process mining analysis.

Common Uses

Simplify dataset views by removing technical or system-generated attributes from display
Hide sensitive information such as personal data or confidential business metrics during presentations
Remove intermediate calculation attributes that were used for other enrichments but are no longer needed
Clean up imported datasets that contain legacy or unused columns from source systems
Focus analysis on specific attribute sets by hiding unrelated metrics
Create role-specific views by hiding attributes not relevant to certain user groups
Prepare cleaner exports by removing unnecessary columns before sharing datasets

Settings

Attribute Name: Select the attribute you want to hide from the dropdown list. The list displays all available attributes in your dataset except for mandatory system columns (Case ID, Activity, Timestamp, Event Index, and Internal Case ID) which cannot be hidden. Only attributes that are currently visible and not calculated by the system are available for selection. Choose the specific attribute you want to remove from the display.

Examples

Example 1: Simplifying Purchase Order Analysis

Scenario: A procurement dataset contains numerous technical fields imported from the ERP system that clutter the view and make it difficult for business analysts to focus on key metrics like costs and approval times.

Settings:

Attribute Name: SAP_Document_Type_Code

Output: The selected attribute "SAP_Document_Type_Code" is removed from the visible dataset. The data remains in the underlying system but is no longer displayed in tables, filters, or analysis views. Users see a cleaner dataset focused on business-relevant attributes like Total_Cost, Approval_Duration, and Vendor_Name.

Insights: By removing technical ERP codes and system fields, analysts can more easily identify patterns in procurement processes without being distracted by implementation-specific attributes that add no analytical value.

Example 2: Privacy Protection for Healthcare Data

Scenario: A hospital's patient flow analysis dataset needs to be shared with external consultants, but certain attributes containing sensitive medical information should not be visible during the engagement.

Settings:

Attribute Name: Patient_Medical_Record_Number

Output: The "Patient_Medical_Record_Number" attribute is hidden from all views while maintaining the dataset's analytical capabilities. The case can still be tracked using the Case ID, but the sensitive medical record identifier is no longer visible in any reports or analysis screens.

Insights: This approach enables secure collaboration with external parties while maintaining patient privacy and compliance with healthcare regulations, all without creating multiple versions of the dataset.

Example 3: Cleaning Up Manufacturing Process Data

Scenario: A manufacturing process dataset includes numerous intermediate calculation fields from previous analyses that are no longer needed and make it difficult to navigate the attribute list.

Settings:

Attribute Name: Temp_Calc_Quality_Score_v1

Output: The temporary calculation attribute "Temp_Calc_Quality_Score_v1" is removed from view. The final "Quality_Score" attribute remains visible, and users no longer see the intermediate calculation fields that were used during development but are not needed for ongoing analysis.

Insights: Removing obsolete calculation fields streamlines the dataset, making it easier for users to find and work with current, relevant attributes while reducing confusion about which metrics to use.

Example 4: Focus Financial Audit Analysis

Scenario: An accounts payable audit dataset contains both operational and financial attributes, but auditors need to focus exclusively on financial controls and compliance-related fields.

Settings:

Attribute Name: Vendor_Contact_Email

Output: The "Vendor_Contact_Email" attribute is hidden, allowing auditors to concentrate on financial attributes like Invoice_Amount, Payment_Terms, and Approval_Hierarchy without distraction from operational contact information.

Insights: Creating a focused view helps auditors efficiently identify financial control issues and compliance violations without being overwhelmed by operational details that are outside their audit scope.

Example 5: Streamlining Order Fulfillment Dashboard

Scenario: An e-commerce order fulfillment dataset includes detailed product attributes that are not relevant for process performance analysis and slow down dashboard rendering.

Settings:

Attribute Name: Product_Description_Long

Output: The lengthy "Product_Description_Long" text attribute is hidden from the dataset view. Performance metrics, delivery times, and order statuses remain visible, creating a more responsive and focused analytical environment. The dashboard loads faster and is easier to navigate.

Insights: Removing verbose text fields that don't contribute to process analysis improves both system performance and user experience, allowing analysts to focus on process efficiency metrics rather than product details.

Output

The Hide Attribute enrichment modifies the dataset's visible structure without altering the underlying data. The selected attribute is removed from:

Case and event attribute lists in the user interface
Filter selection dropdowns
Export operations (unless specifically configured otherwise)
Calculation and enrichment attribute selectors
Data preview tables and grids

The hidden attribute remains in the dataset's internal structure and can potentially be restored through dataset configuration if needed. No new attributes are created by this enrichment - it only affects the visibility of existing attributes. Other enrichments that previously referenced the hidden attribute will continue to function normally as the data remains intact in the background.

Overview

Common Uses

Settings

Examples

Example 1: Simplifying Purchase Order Analysis

Example 2: Privacy Protection for Healthcare Data

Example 3: Cleaning Up Manufacturing Process Data

Example 4: Focus Financial Audit Analysis

Example 5: Streamlining Order Fulfillment Dashboard

Output

See Also