A transformation is a schema object that typically maps a specified time period to another time period, applying an offset value, such as current month minus one month.
Usually defined by a project designer, transformations are used in the definition of a metric to alter the behavior of that metric. Such a metric is referred to as a transformation metric. For example, time-related transformations are commonly used in metrics to compare values at different times, such as this year versus last year or current date versus month-to-date. Any transformation can be included as part of the definition of a metric and multiple transformations can be applied to the same metric. Transformation metrics are beyond the scope of this guide; for information about transformation metrics, see the Advanced Reporting Help.
Recall the example used in the introduction, the TY/LY comparison. To calculate this year's revenue, you can use the Revenue metric in conjunction with a filter for this year. Similarly, to calculate last year's revenue, you can use the Revenue metric in conjunction with a filter for last year. However, a more flexible alternative is to use a previously created Last Year transformation in the definition of a new metric, last year's revenue. With a single filter, on 2003 for example, the two metrics Revenue and Last Year Revenue give you results for 2003 and 2002, respectively.
Since a transformation represents a rule, it can describe the effect of that rule for different levels. For instance, the Last Year transformation intuitively describes how a specific year relates to the year before. It can in addition express how each month of a year corresponds to a month of the prior year. In the same way, the transformation can describe how each day of a year maps to a day of the year before. This information defines the transformation and abstracts all cases into a generic concept. That is, you can use a single metric with a last year transformation regardless of the time attribute contained on the report.
While transformations are most often used for discovering and analyzing time-based trends in your data, not all transformations have to be time-based. An example of a non-time-based transformation is This Catalog/Last Catalog, which might use
Catalog_ID-1 to perform the transformation.