Understanding Attributes
You can use the Essbase attribute feature to retrieve and analyze data not only from the perspective of dimensions, but also in terms of characteristics, or attributes, of those dimensions. For example, you can analyze product profitability based on size or packaging, and you can make more effective conclusions by incorporating market attributes, such as the population of each market region, into the analysis.
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.
A few ways analysis by attribute provides depth and perspective, supporting better-informed decisions:
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You can select, aggregate, and report on data based on common features (attributes).
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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.
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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.
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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.
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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.
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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