ComplianceCheckNodeEditor Documentation

Overview

The ComplianceCheckNodeEditor is a specialized editor component for configuring compliance check rules within the Vantage analytics and data platform. Its primary purpose is to allow users to define ordered compliance rules that validate data against specific conditions. Rows that fail to conform to any defined rule are directed to a "Fail" category, alongside notes indicating the reason for the failure. In contrast, rows passing all rules are classified under "Pass."

How it Works

1. Rule Configuration: Users can add and customize multiple compliance rules. Each rule specifies:

   • A label for identification.
   • A data column from the upstream data.
   • An operator defining the check condition.
   • A value against which the data in the specified column is evaluated.

2. Dynamic Management: Users can modify the order of rules, update rule parameters, or remove rules as needed. The interface provides validation cues to help users understand the input and potential errors.

3. Integration with Upstream Data: The editor fetches available columns from the upstream data context, enabling users to select the appropriate column for each rule.

Settings

The ComplianceCheckNodeEditor has a variety of settings for configuring the compliance check rules:

Compliance Rules

• Setting Name: rules
• Input Type: Array of Objects
• Description: This setting holds all the compliance rules defined by the user. Each rule within the array contains properties such as column, operator, value, and label. Altering this setting changes the compliance checks applied to the dataset, which directly impacts which rows pass or fail.
• Default Value: An empty array [].

Rule Elements

Each rule object consists of the following fields:

1. Label

   • Setting Name: label
   • Input Type: String
   • Description: A user-defined name for the rule that helps identify its purpose. Changing this updates the textual representation of the rule in the UI.
   • Default Value: An empty string "".

2. Column

   • Setting Name: column
   • Input Type: Dropdown (String)
   • Description: A selector to choose from available columns in the upstream dataset. Selecting a column will link the rule to the specified data. If no column is selected, the rule will be invalid, leading to a compliance failure.
   • Default Value: An empty string "".

3. Operator

   • Setting Name: operator
   • Input Type: Dropdown (String)
   • Description: This selector allows the user to choose the type of comparison to perform on the data. The selected operator dictates how the rule evaluates the column data. Changing the operator can significantly affect the outcomes of compliance checks.
   • Default Value: isNotEmpty.

4. Value

   • Setting Name: value
   • Input Type: String (optional, based on operator)
   • Description: This input field captures the specific criterion against which the column data is evaluated. It is conditionally displayed based on the selected operator. Not all operators require a value; for example, the operator "isEmpty" does not need a value.
   • Default Value: An empty string "".

Use Cases & Examples

Use Cases

1. Data Quality Assurance: Businesses can leverage the ComplianceCheckNodeEditor to ensure that data integrity is maintained by validating entries against predefined compliance standards, such as ensuring that required fields are not empty.

2. Regulatory Compliance: Organizations can configure rules to meet regulatory requirements by validating that data adheres to specific formats, values, or absence thereof, ensuring compliance with laws such as GDPR or HIPAA.

3. Operational Analytics: Analysts can define rules to filter out irrelevant data, helping improve the quality of insights drawn from operational metrics by bypassing anomalous or unexpected entries.

Example Configuration

Use Case: Validating that all employee email addresses in a dataset are correctly formatted and that no fields are empty.

Configuration Steps:

1. Add Rules:

   • Rule 1:

     • Label: "Check email format"
     • Column: "Email"
     • Operator: "regex"
     • Value: "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9-]+.[a-zA-Z]{2,6}$"

   • Rule 2:

     • Label: "Ensure no empty entries"
     • Column: "Name"
     • Operator: "isNotEmpty"
     • Value: ""

2. Result Interpretation:

   • Rows where the email format does not match the regex or where the Name column is empty will fail the compliance check and be routed to a "Fail" category.

By utilizing the ComplianceCheckNodeEditor, organizations can implement strong data governance mechanisms, thereby ensuring high data quality and compliance with relevant standards and regulations.