Collate Automations

Overview

Collate's Automation feature is a powerful tool designed to simplify and streamline metadata management tasks. By automating repetitive actions such as assigning owners, domains, or tagging data, Collate helps maintain consistency in metadata across an organization's datasets. These automations reduce manual effort and ensure that metadata is always up-to-date, accurate, and governed according to predefined policies.

Why Automations are Useful

Managing metadata manually can be challenging, particularly in dynamic environments where data constantly evolves. Collate's Automation feature addresses several key pain points:

• Maintaining Consistency: Automation helps ensure that metadata such as ownership, tags, and descriptions are applied consistently across all data assets.
• Saving Time: Automations allow data teams to focus on higher-value tasks by eliminating the need for manual updates and maintenance.
• Enforcing Governance Policies: Automations help ensure that data follows organizational policies at all times by automatically applying governance rules (e.g., assigning data owners or domains).
• Data Quality and Accountability: Data quality suffers without clear ownership. Automating ownership assignments helps ensure that data quality issues are addressed efficiently.

Key Use Cases for Collate Automations

1. Bulk Description

Getting started with Automation

• Problem: Many datasets lack descriptions, making it difficult for users to understand the data's purpose and contents. Sometimes, the same column description needs to be added to multiple datasets.
• Solution: Automations can bulk-apply descriptions to tables and columns, ensuring that all data assets are consistently documented.
• Benefit: This use case improves data discoverability and understanding, making it easier for users to find and use the data effectively.

For the Action Configuration:

• Apply to Children: Lets you apply the description to the selected child assets (e.g., columns) within an asset.
• Overwrite Metadata: Allows you to overwrite existing descriptions with the new description. Otherwise, we will only apply the description to empty tables or columns.

2. Bulk Ownership and Domain Assignment

Getting started with Automation

• Problem: Many data assets lack proper ownership and domain assignment, leading to governance and accountability issues. Manually assigning owners can be error-prone and time-consuming.
• Solution: Automations can bulk-assign ownership and domains to datasets, ensuring all data assets are correctly categorized and owned. This process can be applied to tables, schemas, or other assets within Collate.
• Benefit: This use case ensures data assets have a designated owner and are organized under the appropriate domain, making data more discoverable and accountable.

For the Action Configuration:

• Overwrite Metadata: Allows you to overwrite existing owner or domain with the configured one. Otherwise, we will only apply the owner or domain to assets that do not have an existing owner or domain.

3. Bulk Tagging and Glossary Term Assignment

Getting started with Automation

• Problem: Manually applying the same tags or glossary terms to multiple datasets can be inefficient and inconsistent.
• Solution: Automations allow users to bulk-apply tags (e.g., PII) or glossary terms (e.g., Customer ID) to specific datasets, ensuring uniformity across the platform.
• Benefit: This automation reduces the risk of missing important tags like PII-sensitive and ensures that key metadata elements are applied consistently across datasets.

For the Action Configuration:

• Apply to Children: Lets you apply the Tags or Glossary Terms to the selected child assets (e.g., columns) within an asset.
• Overwrite Metadata: Allows you to overwrite existing Tags or Terms with the configured one. Otherwise, we will add the new Tags or Terms to the existing ones.

4. Metadata Propagation via Lineage

Getting started with Automation

• Problem: When metadata such as tags, descriptions, or glossary terms are updated in one part of the data lineage, they may not be propagated across related datasets, leading to inconsistencies.
• Solution: Use automations to propagate metadata across related datasets, ensuring that all relevant data inherits the correct metadata properties from the source dataset.
• Benefit: Metadata consistency is ensured across the entire data lineage, reducing the need for manual updates and maintaining a single source of truth.

For the Action Configuration:

1. First, we can choose if we want the propagation to happen at the Parent level (e.g., Table), Column Level, or both. This can be configured by selecting Propagate Parent and/or Propagate Column Level.
2. Then, we can control which pieces of metadata we want to propagate via lineage:
   • Propagate Description: Propagates the description from the source asset to the downstream assets. Works for both parent and column-level.
   • Propagate Tags: Propagates the tags from the source asset to the downstream assets. Works for both parent and column-level.
   • Propagate Glossary Terms: Propagates the glossary terms from the source asset to the downstream assets. Works for both parent and column-level.
   • Propagate Owners: Only applicable for Parent assets. Propagates the owner information to downstream assets.
   • Propagate Tier: Only applicable for Parent assets. Propagated the tier information to downstream assets.

As with other actions, you can choose to Overwrite Metadata or keep the existing metadata and only apply the new metadata to assets that do not have the metadata already.

5. Automatic PII Detection and Tagging

Getting started with Automation

• Problem: Manually identifying and tagging Personally Identifiable Information (PII) across large datasets is labor-intensive and prone to errors.
• Solution: Automations can automatically detect PII data (e.g., emails, usernames) and apply relevant tags to ensure that sensitive data is flagged appropriately for compliance.
• Benefit: Ensures compliance with data protection regulations by consistently tagging sensitive data, reducing the risk of non-compliance.

Best Practices

• Validate Assets Before Applying Actions: Always use the Explore page to verify the assets that will be affected by the automation. This ensures that only the intended datasets are updated.
• Use Automation Logs: Regularly check the Recent Runs logs to monitor automation activity and ensure that they are running as expected.
• Propagate Metadata Thoughtfully: When propagating metadata via lineage, make sure that the source metadata is correct before applying it across multiple datasets.