Data Taxonomy

Taxonomy represents a formal structure of classes or types of objects within a knowledge domain by using a controlled vocabulary to make it easier to find related information. Defining and using a taxonomy can offer additional benefits in that users of the system will be categorising content and assets using a controlled vocabulary. This controlled vocabulary can be utilised as an integration reference point between different business systems.

A taxonomy must:

• Follow a hierarchical format and provides names for each object in relation to other objects.
• Have specific rules used to classify or categorize any object in a domain. These rules must be complete, consistent, and unambiguous.
• Apply rigor in specification, ensuring any newly discovered object must fit into one and only one category or object.
• Inherit all the properties of the class above it, and can also have additional properties.
• May also capture the membership properties of each object in relation to other objects.

A data taxonomy is a hierarchical structure separating data into specific classes of data based on common characteristics. The taxonomy represents a convenient way to classify data to prove it is unique and without redundancy. This includes both primary and generated data elements.

Taxonomies are different from metadata in that a taxonomy helps you to organize your content and assets into hierarchical relationships. Classifying content and assets in a taxonomy can make it far easier to search for or browse an asset or content management system when you aren't sure exactly what you are looking for.

Defining and using a taxonomy can offer additional benefits in that users of the system will be categorizing content and assets using a controlled vocabulary. This controlled vocabulary can be utilized as an integration reference point between different business systems.

Organisations apply taxonomies to:

• Achieve better data quality
• Organize metadata in an easy grasp format
• Manage data assets through data governance
• Make it easier for a data steward to curate information
• Guide machine learning and data experiences towards identifying trends and patterns.