Attributes describe characteristics of data such as product size and color. Through attributes, you can group and analyze members of dimensions based on their characteristics.
Attribute analysis can tell you, for example, that decaffeinated drinks sold in cans in small markets are less profitable than you had anticipated. For more details, you can filter your analysis by specific attribute criteria, including minimum or maximum sales and profits of different products in similar market segments.
You can select, aggregate, and report on data based on common features, and you can choose from several consolidation methods:
Sums
Counts
Averages
Minimums
Maximums
There are several attribute types:
Text
Numeric
Boolean
Date
As the following examples illustrate, analysis-by-attribute can provide depth and perspective, helping you make better-informed decisions:
You can select, aggregate, and report on data based on common features (attributes).
By defining attributes as having a text, numeric, Boolean, or date type, you can filter (select) data using type-related functions such as AND, OR, NOT, <, >, and = comparisons.
You can use the numeric attribute type to group statistical values by attribute ranges; for example, population groupings such as <500,000, 500,000–1,000,000, and >1,000,000.
You can view sums, counts, minimum or maximum values, and average values of attribute data.
You can perform calculations using numeric attribute values in calculation scripts and member formulas.
You can drill down through data to find out more detailed information, or drill up to see a summary overview of data.