Metadata-detected Data Classes

The data classification process tries to detect and match unique formats of harvested objects. Some harvested objects have nothing unique or detectable about their data. Harvested objects of type DATE or BLOB are good examples. In this case, you can try to identify similar harvested objects using metadata-detected classes.

Metadata-detected class matches harvested objects by their metadata attributes, like name. For example, we can try to classify date of birth columns by their data type, DATE and name that contains DOB.

These date of birth columns can contain PII information. Customers can play safe and mark any of these columns with PII and instruct the application to hide their data. Data and Metadata-detected classes share the same PII and Data Hide infrastructure.

A column can have DOB name and DATE data type but have nothing to do with date of birth columns (e.g. date of bankruptcy). You can approve and reject a matched Metadata-detected class the same way you can do with a Data Class.

A Metadata-detected class matches objects by their attributes using an Metadata Query Language (MQL) query.

Data classification is an operation that users start explicitly. MM performs the metadata-detected data classification automatically, each time you import a harvested model. Harvested metadata is static (cannot be changed between imports). You can change Metadata-detected classes and decide to rerun metadata-detected data classification on the whole repository.