Comparing Attributes and UDAs

Attributes and UDAs enable analysis based on characteristics of the data. Attributes provide greater capability than UDAs. The tables in this topic describe the differences between attributes and UDAs in these areas of functionality:

Table 19. Data Storage—Comparing Attributes and UDAs

Data storage

Attributes

UDAs

You can associate with sparse dimensions.

Supported

Supported

You can associate with dense dimensions.

Not supported

Supported

Table 20. Data Retrieval—Comparing Attributes and UDAs

Data Retrieval

Attributes

UDAs

You can group and retrieve consolidated totals by attribute or UDA value. For example, associate the value High Focus Item to various members of the Product dimension and use that term to retrieve totals and details for only those members.

Supported

Simple

Supported

More difficult to implement, requiring additional calculation scripts or commands

You can categorize attributes in a hierarchy and retrieve consolidated totals by higher levels in the attribute hierarchy; for example, if each product has a size attribute such as 8, 12, 16, or 32, and the sizes are categorized as small, medium, and large. You can view the total sales of small products.

Supported

Supported

More difficult to implement

You can create crosstab views displaying aggregate totals of attributes associated with the same base dimension.

Supported

You can show a crosstab of all values of each attribute dimension.

Not supported

You can retrieve only totals based on specific UDA values.

You can use Boolean operators AND, OR, and NOT with attribute and UDA values to refine a query. For example, you can select decaffeinated drinks from the 100 product group.

Supported

Supported

Because attributes have a text, Boolean, date, or numeric type, you can use appropriate operators and functions to work with and display attribute data. For example, you can view sales totals of all products introduced after a specific date.

Supported

Not supported

You can group numeric attributes into ranges of values and let the dimension building process automatically associate the base member with the appropriate range. For example, you can group sales in various regions based on ranges of their populations—less than 3 million, between 3 million and 6 million, and so on.

Supported

Not supported

Through the Attribute Calculations dimension, you can view aggregations of attribute values as sums, counts, minimums, maximums, and averages.

Supported

Not supported

You can use an attribute in a calculation that defines a member. For example, you can use the weight of a product in ounces to define the profit per ounce member of the Measures dimension.

Supported

Not supported

You can retrieve specific base members using attribute-related information.

Supported

Powerful conditional and value-based selections

Supported

Limited to text string matches only

Table 21. Data Conversion—Comparing Attributes and UDAs

Data Conversion

Attributes

UDAs

Based on the value of a UDA, you can change the sign of the data as it is loaded into the database. For example, you can reverse the sign of all members with the UDA Debit.

Not supported

Supported

Table 22. Calculation Scripts—Comparing Attributes and UDAs

Calculation Scripts

Attributes

UDAs

You can perform calculations on a member if its attribute or UDA value matches a specific value. For example, you can increase the price by 10% of all products with the attribute or UDA of Bottle.

Supported

Supported

You can perform calculations on base members whose attribute value satisfies conditions that you specify. For example, you can calculate the Profit per Ounce of each base member.

Supported

Not supported