Understanding Metadata - 17.1

Metadata is high level information about your data. It is generated when you:

• Import data as entities
• Analyze the data
• Create entities by running projects

In addition to standard information such as creation date and data type, metadata includes value distinctness, soundexes, and metaphones, and findings such as duplicate rows and data conflicts. Metadata also includes statistics about how well your data meets the compliance standards that you specify and also describes the relationships between your data within and across entities. Metadata is available for projects, entities, attributes, and other repository objects.

When you load data into an entity, automatically calculates three types of data statistics to represent the quality of your data:

• Inferred metadata—Statistics derived from the full volume of your enterprise data. Examples of inferred metadata include inferred datatype and inferred scale.
• Documented metadata—Statistics taken directly from the schema (DDL or copybook) during a data import. Documented Datatype is an example of documented metadata.
• Target metadata—Recommendations of how the target (or current) repository object should look based on your actual data. Target datatype is an example of target metadata.

gives you the ability to verify the quality of data in your data source against corporate standards and other compliance requirements that impact your business.