Using data buckets
Data Buckets allow Visualizer users to create a new Attribute based on where a Measure falls in a range of values or as a percentile rank without intervention of a Birst administrator. Data Buckets are similar to the Flash feature Bucketed Measures. Normally, attributes are values that are provided directly in the source data. Attributes in general can be used to group measures. However, it can be useful to define groups based on measures. Data Buckets allow a designer to take a given measure and define ranges of values, or buckets. These buckets can then be used like any other attribute to group other measures.
As an example, suppose you wanted to compare discounting behavior of large versus small customers. If there is no data field assigned to a customer to indicate large versus small, you could create a data bucket to measure each customer and determine their category. If you decided that the difference between large and small customers are those that have an order volume of over 1,000 units vs. under 1,000 units, you would create a data bucket that makes this distinction and name it Customer Size Category. You would specify a category called Large with the minimum value set to 1,000 and the maximum value set to 10,000 and a Small category with a minimum value of 0 and a maximum value of 1000.
Users can edit their existing Bucketed Measures by accessing them from the Subject Area section in Visualizer.