count values

Overview

The Count Values enrichment is a powerful statistical analysis tool that counts the number of distinct (unique) values for a selected event attribute within each case in your process dataset. This enrichment is essential for understanding the variety and diversity of data values across your process instances. It creates a new case-level attribute containing the count of unique values found, providing insights into data complexity, variation patterns, and potential data quality issues.

This enrichment is particularly valuable for analyzing categorical data variation, identifying cases with unusual diversity in attribute values, and understanding process complexity metrics. By counting distinct values rather than total occurrences, it helps identify cases where multiple different values appear for the same attribute, such as different product types ordered, various departments involved, or multiple statuses encountered during case execution.

The enrichment operates at the case level, examining all events within each case to determine how many unique values exist for the specified attribute. This makes it ideal for scenarios where you need to measure variety, complexity, or data richness within individual process instances.

Common Uses

Count the number of different products or SKUs ordered in a single purchase order
Identify how many different departments or teams were involved in processing a case
Measure the variety of error codes or exception types encountered during case execution
Determine the number of unique vendors or suppliers involved in procurement cases
Count distinct customer segments or categories served in a single transaction
Analyze the diversity of approval levels or authorization statuses in approval workflows
Track the number of different systems or applications accessed during case processing

Settings

New Attribute Name: The name for the new case attribute that will store the count of unique values. This should be a descriptive name that clearly indicates what is being counted. For example, if counting unique product types, you might name it "Unique_Product_Count" or "Product_Variety_Count". The attribute will be created as an integer type and displayed with number formatting.

Attribute Name: The event attribute whose unique values will be counted. This dropdown lists all available event attributes in your dataset. Select the attribute that contains the values you want to analyze for uniqueness. The enrichment will examine this attribute across all events in each case to count distinct values.

Allow Null: A checkbox option that determines whether null (empty) values should be included in the unique value count. When checked (true), null values are counted as one distinct value if they appear in the case. When unchecked (false), null values are ignored and not counted. This setting is important for accurate counting when your data may contain missing values.

Filter: An optional filter that can be applied to limit which events are considered when counting unique values. This allows you to count distinct values only from specific activities, time periods, or other filtered subsets of events within each case. When no filter is specified, all events in the case are examined.

Examples

Example 1: Counting Product Variety in Purchase Orders

Scenario: A retail company wants to analyze the complexity of their purchase orders by understanding how many different product SKUs are typically ordered together. This helps optimize warehouse picking processes and identify opportunities for bundling.

Settings:

New Attribute Name: Unique_SKU_Count
Attribute Name: Product_SKU
Allow Null: False (unchecked)
Filter: Activity equals "Add Item to Order"

Output: The enrichment creates a new case attribute "Unique_SKU_Count" containing the number of distinct product SKUs in each order:

Case PO-2024-001: Unique_SKU_Count = 5 (customer ordered 5 different products)
Case PO-2024-002: Unique_SKU_Count = 1 (single product order)
Case PO-2024-003: Unique_SKU_Count = 12 (complex order with many products)

Insights: Orders with high unique SKU counts require more complex warehouse picking operations. The company can use this metric to route orders efficiently and identify opportunities for product bundling based on commonly co-ordered items.

Example 2: Analyzing Department Involvement in IT Tickets

Scenario: An IT service desk wants to understand the complexity of support tickets by counting how many different departments are involved in resolving each ticket. This helps identify tickets that require cross-functional collaboration.

Settings:

New Attribute Name: Departments_Involved_Count
Attribute Name: Assigned_Department
Allow Null: False (unchecked)
Filter: None (analyze all events)

Output: Each IT ticket case receives a "Departments_Involved_Count" attribute:

Case TICKET-5001: Departments_Involved_Count = 1 (handled entirely by Help Desk)
Case TICKET-5002: Departments_Involved_Count = 3 (escalated through Help Desk, Network Team, Security)
Case TICKET-5003: Departments_Involved_Count = 5 (complex issue requiring multiple teams)

Insights: Tickets involving multiple departments have longer resolution times and higher costs. The organization can use this metric to improve routing, establish better collaboration protocols, and identify training needs.

Example 3: Supplier Diversity in Procurement Process

Scenario: A manufacturing company needs to track supplier diversity in their procurement processes to ensure compliance with supplier diversification policies and identify single-source dependencies.

Settings:

New Attribute Name: Unique_Supplier_Count
Attribute Name: Supplier_ID
Allow Null: True (checked)
Filter: Activity contains "Quote" or Activity contains "Purchase"

Output: The enrichment adds "Unique_Supplier_Count" to each procurement case:

Case PROC-2024-101: Unique_Supplier_Count = 4 (received quotes from 4 different suppliers)
Case PROC-2024-102: Unique_Supplier_Count = 1 (single-source procurement)
Case PROC-2024-103: Unique_Supplier_Count = 7 (competitive bidding with many suppliers)

Insights: Cases with only one supplier represent potential supply chain risks. The company implements policies requiring minimum supplier counts for purchases above certain thresholds and monitors compliance through this metric.

Example 4: Error Type Analysis in Manufacturing

Scenario: A manufacturing plant wants to understand the variety of quality issues encountered during production runs to prioritize quality improvement initiatives and identify problematic production lines.

Settings:

New Attribute Name: Distinct_Error_Types
Attribute Name: Quality_Error_Code
Allow Null: False (unchecked)
Filter: Activity equals "Quality Check Failed"

Output: Each production batch case gets a "Distinct_Error_Types" count:

Case BATCH-2024-A001: Distinct_Error_Types = 0 (no quality issues)
Case BATCH-2024-A002: Distinct_Error_Types = 2 (two different types of defects found)
Case BATCH-2024-A003: Distinct_Error_Types = 5 (multiple quality problems indicating systemic issues)

Insights: Batches with high distinct error counts indicate systemic quality problems requiring immediate attention. The plant uses this metric to trigger comprehensive quality reviews and preventive maintenance when thresholds are exceeded.

Example 5: Customer Interaction Channel Analysis

Scenario: A customer service center wants to understand how many different communication channels customers use during their service journey to optimize omnichannel support strategies.

Settings:

New Attribute Name: Communication_Channels_Used
Attribute Name: Interaction_Channel
Allow Null: False (unchecked)
Filter: None (count all customer interactions)

Output: The enrichment creates a channel diversity metric for each customer case:

Case CUST-2024-1001: Communication_Channels_Used = 1 (phone only)
Case CUST-2024-1002: Communication_Channels_Used = 3 (phone, email, chat)
Case CUST-2024-1003: Communication_Channels_Used = 4 (phone, email, chat, social media)

Insights: Customers using multiple channels often indicate complex issues or frustration with single-channel resolution. The company improves channel integration and ensures consistent information across all touchpoints for better customer experience.

Output

The Count Values enrichment creates a single new case-level attribute with the following characteristics:

Attribute Type: Integer (Int32) - The count is always a whole number representing the number of unique values found.

Attribute Naming: The new attribute uses the name specified in the "New Attribute Name" setting. Choose descriptive names that clearly indicate what is being counted.

Display Format: The attribute is automatically formatted as a number in the dataset view, making it easy to sort, filter, and analyze.

Value Range: The count ranges from 0 (when no matching values are found or all values are null with "Allow Null" unchecked) to the maximum number of events in a case (when every event has a different value).

Integration: The new attribute can be used immediately in:

Filters to identify cases with specific unique value counts
Calculators for statistical analysis and aggregations
Other enrichments that require numeric case attributes
Process mining visualizations and dashboards
Export operations for external analysis

Overview

Common Uses

Settings

Examples

Example 1: Counting Product Variety in Purchase Orders

Example 2: Analyzing Department Involvement in IT Tickets

Example 3: Supplier Diversity in Procurement Process

Example 4: Error Type Analysis in Manufacturing

Example 5: Customer Interaction Channel Analysis

Output

See Also