Derived Data lets you to reference results in new queries, query stacking, and eases cohort analysis. In CDP Data Visualization, we use derived data for computed fields in data modeling, weighted sums and averages, custom binning, for set-based and group-based analysis, and for combining data from different data sources.
Derived data enables you to reference query results in new queries, in essence "stacking" results from sub-select queries. The derived data feature also supports cohort analysis, where a set of data from a report is used (joined back) in another report, and allows you to build computed columns for re-use.
Derived Data is very useful in determining weighted averages and other, more complex
calculations. For example, in the dataset
World Life Expectancy, life
expectancy is reported at the level of each country, for each year. If we wanted to
determine the life expectancy by region or subregion, we have to calculate a weighted
average of life expectancies. You can also parametrize derived data definitions using
The following steps demonstrate how to use derived data on a table visual based on the
World Life Expectancy [data source
samples.world_life_expectancy]. The initial set-up follows:
- Place the fields
countryon the Dimension shelf.
- Place the field
sum(population), on the Measures shelf.
- Place the field
yearon the Filters shelf, and change the expression to
[year]=<<year_param:2000>>. This enables you to dynamically change derived data calculations. You must specify a default value in the parametrized expression.
- Defining Derived Data
- Defining Additional Derived Data
- Using Derived Data
- Viewing Derived Data Definitions
- Saving Derived Data
- Deleting Derived Data Definitions