What Are Attributes?

Attributes are individual pieces of information that describe characteristics of a dimension. Each attribute is linked to one or more columns in a dimension table and helps define how data can be grouped, filtered or analyzed. Attributes can be and are generally arranged into user-defined hierarchies that provide the drill-down paths by which users can browse the data in the measure groups to which the attribute is related. Attributes like colors, flavors, or sizes are types of information that describe a product or item, but they are not part of a structured hierarchy as they don’t have levels above or below them.

Attributes can be arranged into hierarchies to support drill-down analysis (e.g., Year → Quarter → Month) or they can exist independently (like color or size) without supporting any hierarchical navigation.

Users can use dimension attributes as part of their analysis to tease out more information, for example, if a user is researching airline travel habits they could see on which days of the week most people travel for business travel, vacation travel, etc. Or they can use them to find out which colors were most popular for a particular season or year or garment.

What Are Level Attributes?

Level attributes provide additional information about the dimension members at a particular level of a dimension hierarchy. The dimension members themselves may be meaningless, such as a value of “365” for a time period. These values for dimension members are used internally for selecting and sorting quickly but are meaningless to users.

For example, users might have columns for

• Employee number (ENUM)
• Last name (LAST_NAME)
• First name (FIRST_NAME) and
• Telephone extension (TELNO)

Here, ENUM also has a NUMBER data type, which makes it more efficient than a text column for the creation of indexes. LAST_NAME, FIRST_NAME and TELNO are attributes.

What Are Dimension Attributes?

Dimension attributes specify groupings of level attributes for a specific dimension. Whereas level attributes map to specific data values, dimension attributes are purely logical metadata objects.

An example of a dimension attribute is end date, which is required for time dimensions. If a time dimension has month, quarter and year levels, end date identifies the last date of each month, each quarter and each year.

Understanding attributes and their types helps users navigate complex datasets with clarity. Whether defining drill-down paths through hierarchies or enabling more flexible, ad hoc analysis with independent attributes, they form the backbone of meaningful data organization. When modeled well, attributes empower business users to extract accurate, contextual insights with ease.