Performance, usability, and version control are all fundamental characteristics of effective data models but often it's the additional analytical context that set models apart. Once fundamental measures have been implemented, additional DAX measures can be developed to support common and high priority business analysis. These measures can often replace ad hoc and manual data analysis for business users as well as dedicated custom reports maintained by the BI organization. As measures are stored within the data model, the logic can be re-used in various combinations and in future projects.
In this recipe DAX measures are created to support deeper pricing analysis. Additionally, an example of computing the geometric mean at day, month, and year grains is provided.
