Benefits of Essbase Attributes

Use attributes to analyze data not only from the perspective of dimensions, but also in terms of characteristics (attributes) of dimensions. For some situations, you might consider an alternative approach to attribute dimensions.

Use Cases for Attributes

You can use attributes to analyze product profitability based on packaging, or make conclusions based on market attributes such as the population of a sales region.

Such an analysis could tell you that decaffeinated drinks sold in cans in small markets (populations less than 6,000,000) are less profitable than you anticipated. For more details, you can filter the analysis by specific attribute criteria, including minimum or maximum sales and profits of different products in similar market segments.

The following list explains a few ways analysis by attribute provides depth and perspective, supporting 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, and NOT operators and <, >, 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.
Through the Attribute Calculations dimension automatically created by Essbase, you can view sums, counts, minimum or maximum values, and average values of attribute data. For example, when you enter Avg and Bottle into a spreadsheet, Essbase retrieves calculated values for average sales in bottles for all the column and row intersections on the sheet.
You can perform calculations using numeric attribute values in calculation scripts and member formulas; for example, to determine profitability by ounce for products sized by the ounce.
You can create crosstabs of attribute data for the same dimension, and you can pivot and drill down for detail data in spreadsheets.

An attribute crosstab is a report or spreadsheet showing data consolidations across attributes of the same dimension. The crosstab example below displays product packaging as columns and the product size in ounces as rows. At their intersections, you see the profit for each combination of package type and size.

From this information, you can see which size-packaging combinations were most profitable in the Florida market.
```
Product Year Florida Profit Actual
        Bottle     Can       Pkg Type
        ======     =====     ========
  32      946        N/A        946
  20      791        N/A        791
  16      714        N/A        714
  12      241      2,383      2,624
Ounces  2,692      2,383      5,075
```

Benefits of Attributes vs Other Approaches

For the most flexibility and functionality, use attribute dimensions to define attribute data. Using attribute dimensions provides the following features:

Sophisticated, flexible data retrieval

You can view attribute data only when you want to; you can create meaningful summaries through crosstabs; and, using type-based comparisons, you can selectively view only the data that you want to see.
Additional calculation functionality

Not only can you perform calculations on the names of members of attribute dimensions to define members of standard dimensions, you can also access five types of consolidations of attribute data—sums, counts, averages, minimums, and maximums.
Economy and simplicity

Because attribute dimensions are sparse, Dynamic Calc, they are not stored as data. Compared to using shared members, outlines using attribute dimensions contain fewer members and are easier to read.

For some situations, you might consider an alternative approach to attribute dimensions.

Table 6-1 Considering Alternatives to Attribute Dimensions

Situation	Alternative
Analyze attributes of dense dimensions	UDAs or shared members - see Comparison of Attributes and UDAs.
Perform batch calculation of data	Shared members or members of separate, standard dimensions. A disadvantage of shared members is that the outline becomes large if the categories repeat many members.
Define the name of a member of an attribute dimension as a value that results from a formula	Shared members or members of separate, standard dimensions.
Define attributes that vary over time	Members of separate, standard dimensions. For example, to track product maintenance costs over time, the age of the product at the time of maintenance is important. However, using the attribute feature, you could associate only one age with the product. You need multiple members in a separate dimension for each time period that you want to track.
Minimize retrieval time with large numbers of base-dimension members	Batch calculation with shared members or members of separate, standard dimensions.
Perform cross-dimensional analysis with low performance impact	Non-aggregating attributes