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Data vs. Metadata (or Semantic) classification

Of special importance are personally identifiable information, or Personally Identifiable Information (PII). Automation and completeness of the identification and data classification of the PII objects in the data is fundamental to these activities. In many cases, data classes may be used to profile and match the criteria by which PII may be isolated. In this case, it is the actual data that is analyzed and used to classify the data element. This feature is referred to as data-detected data classification. Data-detected data classification is good at detecting common data patterns automatically but less focused on providing definitions

Some PII harvested objects, like Maiden Name and Date of Birth do not have unique data patterns and cannot be discovered using the data-detected data classification. TDC helps you to identify these types of harvested objects using the metadata-detected data classification. This feature is referred to as metadata-detected data classification . Metadata detected data classification is good at providing authoritative and common definitions. It is more flexible but less precise than data-detected data classification.

A harvested object can have multiple metadata detected data classifications (relationships with business terms). It can have one definition or data-detected data classification and many other semantic or metadata detected data classifications. I.e., you may classify with the same business term several harvested objects that have different data types and patterns.

A harvested object can have multiple proposed, approved and assigned data classifications (relationship with data classes). TDC encourages users to be as precise as possible with the data classifications and strive for a harvested object to have one approved or assigned data classification.

In addition, in terms of semantic lineage, TDC uses data and metadata-detected data classifications to implement lookups of the inferred definition and related elements.

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