Metadata is information about the data collected. According to the DAMA International Data Management Book of Knowledge (DMBOK), Metadata “includes information about technical and business processes, data rules and constraints, and logical and physical data structures.” Think of it as a wrapper around data that describes it, like how packaging tells what food is in a box or a container. In the past, people interacted with Metadata through cards in a library catalog, with technical advances, humans and computers have access to distinct types of Metadata:
- Business Metadata: Provides the meaning of data, by defining terms in every-day language without regard to technical implementation. It “focuses largely on the content and condition of the data and includes details related to Data Governance.”
- Technical Metadata: Provides information on the format and structure of the data as needed by computer systems. Some examples of Technical Metadata include physical database tables, access permissions, data models, backup rules, mapping documentation, data lineage, and many more.
- Operational Metadata: This type of Metadata “describes details of the processing and accessing of data.” (DMBOK) Various example of Operational Metadata include: job execution logs, data sharing rules, error logs, audit results, various version maintenance plans, archive and retention rules, among many others.
Other definitions of Metadata include:
- “Information describing various facets of an information asset, improving its” usability through its life cycle.” (Gartner)
- “Data about Data that unlocks the Who, How, Where, Why and What value of the Data.” (Dr. Peter Aiken)
- “Repositories maintained by Data Architecture.” (John Singer)
- “Various kinds of information – a location, a date or a card catalog item.” (Microsoft)
- “Machine understandable information about web resources or other things.” (W3C)
- “Data about Data.” (NIH)
Businesses apply Metadata to:
- Improve Data Quality.
- Govern Big Data.
- Provide Context about Data.
- Provide Data Services.
- Providing a Data Lineage.
- Facilitate Machine Learning.
- Define data.
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