Types ofMetadata – Data Democratization, Governance, and Security

Types ofMetadata

Metadata is classified by the type of user that is going to use it; e.g., business metadata and technical metadata.

The other method is to classify the metadata by its usage, as follows:

•    Intrinsic metadata is the information that is extracted directly from the data itself; for example, name of the document, size of the document, and content of the document.

•    Administrative metadata is the information that is used to manage the data; for example, who created the document, date created, and date revised.

•    Descriptive metadata is the information that describes the data; e.g.,title of the document, subject of the document, who the audience is, etc.

•    Semantic metadata describes the data’s ability to extract content within the metadata; intelligent metadata.

Business metadata is defined as data about the business data, which includes asset catalogs, document reference libraries, data mining metadata, corporate policy and procedure library, and standards and best practices. This is used by business SMEs, business analysts, IT data stewards, business data stewards, business analysts, and data mining analysts.

Technical metadata is data about technology and is used by technical teams. Business metadata is data about the business and is used majorly by business teams. There is another category/type of metadata that is used by both technical and

business teams.

Classes ofMetadata

There are five classes of metadata, as follows.:

 1.  Source metadata

2. Data integration metadata

3. Data warehouse metadata

4. Metrics and reporting metadata

5. Reference metadata

Source metadata is data about various sources in physical files, databases, copybooks, procedures, parameters and schedules, and transaction flows. This metadata is used by project managers, system managers, operations, developers, testers, integrators, and production support.

Data integration metadata is data about data transformation logic, conversion matrix, business rules and reformatting rules, extraction history, and reconciliation rules, to name a few examples.

Data warehouse metadata is data about reference subject area, logical entities, domains and classes, process models, business definitions, external data, data warehouses, and data marts, for example.

Metrics and reporting metadata is data about lists of data marts, objects, classes, lists of dimensions and fact tables, list of reports, meaning of data, list of data stewards, and data quality metrics, to name a few.

Reference metadata is data about asset catalog, document reference libraries, data mining metadata, corporate policies and procedures library, and standards and best practices, for example.