Data Policy Template
Deprecation Notice (Effective ADOC v26.2.0)
Data Policy Templates have been deprecated as part of a shift toward direct rule-based configurations. Users can continue to implement anomaly detection logic using rulesets, which provide equivalent and more flexible functionality.
A Data Policy Template is a reusable collection of rule definitions that can be applied to multiple Data Quality Policies. Instead of defining rules every time you create a policy, you can create a single template containing multiple rules. When you add this template to a policy, all the rules it contains are automatically evaluated.
Benefits:
- Saves time by reusing common rule definitions
- Ensures consistency across multiple policies
- Simplifies policy creation for new datasets
Adding a Data Policy Template
Follow these steps to create a new Data Policy Template:
- Navigate to: Data Reliability > Manage Policies > Data Policy Templates tab.
- Click Add Data Policy Template to open the configuration page.
- Enter the following details:
- Name: Provide a descriptive name for the template (e.g., Customer Table Quality Rules).
- Description: Explain why this template is being created.
- Select Rules: Choose the rules to include in the template.
Measurement Types and Rule Definitions
Measurement Type | Description | Example |
Null Values | Verifies whether selected columns contain null values | Customer email should not be null |
Schema Match | Checks if column data types match the expected type | Customer ID must be Integer |
Pattern Match | Ensures column values follow a regular expression | Emails must match ^[\w.%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$ |
Enumerations | Validates values against a predefined list | Status must be Active, Inactive, or Pending |
Tags Match | Ensures values are present in a specified tag | Product category must match a defined tag |
Range Match | Validates values fall within a lower and upper bound | Order total between 0 and 10,000 |
Uniqueness Check | Ensures values in a column are unique | Customer ID column must be distinct |
Duplicate Row Check | Detects duplicate rows in a dataset | Ensure no repeated transaction records |
Row Check | Validates total number of rows falls within a range | Table must have 10,000–12,000 rows |
Lookup | Compares values with a reference table or column; optional SQL filter | Check IDs exist in a reference table where is_active = 'Y' |
- (Optional) Add Labels by clicking Add Labels and providing key-value pairs for easier categorization.
- Click Save to create the template.
Understanding the Data Policy Templates Table
The Data Policy Templates table provides an overview of all templates you have created. You can search for a template by its name.
Column Name | Description |
Name | Name of the data policy template |
Description | Description provided when the template was created |
Data Dimensions | Lists data dimensions defined in the template (e.g., Consistency, Uniqueness) |
Labels | Displays any labels associated with the template |
Created At | Date and time the template was created |
Updated At | Most recent date and time the template was updated |
Vertical Ellipsis Icon | Click to edit or delete the template |
Editing a Data Policy Template
- Go to the Data Policy Templates tab in Manage Policies.
- Click the name of the template you want to edit.
- Update the rules, description, or labels as needed.
- Click Save to apply the changes.
Example:
- You have a template Customer Table Rules with 5 rules.
- You add a new Pattern Match rule to check email formats.
- After saving, all policies using this template will now automatically include the new rule.
No comments yet.