Creating custom and conditional columns
Business users often extend the of existing reports and data models with additional to help them analyze and present data. The logic of these columns is generally implemented through Excel formulas or as calculated DAX columns. A superior solution, particularly if the logic cannot quickly be migrated to a data warehouse or IT resource, is to create the columns via the Query Editor and M language.
Developing custom can also enhance the ease-of-use and analytical power of data models and the visualizations they support. In the examples of this recipe, columns are created to simplify the analysis of a customer dimension via existing columns and to apply a custom naming format.
How to do it...
Create a dynamic banding attribute
The goal of this is to create an attribute on the Customer dimension table that groups the customer into age ranges to support demographic analysis:
- Retrieve the current dimension table with the date column to be used for segmentation...
