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Data Tagging and Classification in Data 360
Data tagging is the process of attaching labels or metadata to data assets such as objects, fields, or records to describe their content, sensitivity, or purpose. Tags help identify and organize data so it can be easily searched, filtered, and governed.
Tagging transforms data governance from a manual task into a scalable, policy-driven approach. By linking policies to tags, enforcement becomes dynamic and adapts as data grows. Classification builds on this by grouping tags based on data value, sensitivity, or regulations, such as public, internal, or confidential. Together, tagging and classification help organizations understand their data and apply security, access, and compliance controls more effectively.
Benefits
Instead of applying governance controls manually to each object or field, you can use standardized tags and classification levels to automate and streamline policy enforcement.
Tagging and classification enables scalability in various ways.
- Policy automation: Tags and classifications serve as triggers for governance policies. Once tagged, objects automatically inherit relevant access and masking policies, eliminating repetitive, manual configuration.
- Consistency across data: Applying a defined taxonomy ensures data is classified uniformly across teams, sources, and data types. This reduces ambiguity and minimizes policy gaps.
- Bulk enforcement through tag propagation: Object-level tags propagate to related downstream objects, and field-level tags propagate to the corresponding fields within those objects.
- Simplified governance for new data: The Suggest Tags feature uses LLM-based tagging to recommend appropriate tags and classifications based on metadata, making it easier to govern newly ingested or created data with minimal manual effort.
- Attribute-based access control (ABAC): Tags and classifications can be used as attributes in access policies, supporting fine-grained control based on data context rather than hardcoded roles or objects.
Tagging and classification decouple governance from individual data objects, allowing you to apply policies at scale with speed, accuracy, and reduced manual effort.
- Object-Level and Field-Level Tags in Data 360
In data governance, you can use object-level tags and field-level tags to label and manage data at different levels of granularity. - Manual and Automatic Tagging in Data 360
There are two ways to apply tags to data: manual tagging and automatic tagging. - Tag Propagation in Data 360
Tag propagation helps maintain consistent data classification by extending tags from a primary data object to related or downstream objects and fields. This ensures that related objects in your data model carry the same metadata labels and reduces the need for repetitive tagging. - Parent-Child Hierarchy in Data 360
A parent-child tag hierarchy organizes tags in a logical, multi-level structure, where broader, general tags (parents) are linked to more specific, detailed tags (children). - Data Governance Taxonomy
In Data 360 data governance, a taxonomy is a hierarchical set of categories and subcategories (or tags) that classifies data based on its type, sensitivity, usage, or domain. Using taxonomies ensures consistent tagging, improves data discoverability, and enables accurate application of policies such as masking or access control. They also help align data classification with privacy and compliance requirements. - Sensitive Data Classification in Data 360
Classifying data helps ensure it’s handled appropriately, securely, and in line with regulatory or internal standards. You can classify data by considering both its sensitivity (how confidential it is) and its usage (how and by whom it’s accessed or used).
