Benefits of "snowflaking"

* Some OLAP multidimensional database modeling tools that use dimensional data marts as a data source are optimized for snowflake schemas.
* If a dimension is very sparse (i.e. most of the possible values for the dimension have no data) and/or a dimension has a very long list of attributes which may be used in a query, the dimension table may occupy a significant proportion of the database and snowflaking may be appropriate.
* A multidimensional view is sometimes added to an existing transactional database to aid reporting. In this case, the tables which describe the dimensions will already exist and will typically be normalized. A snowflake schema will therefore be easier to implement.
* A snowflake schema can sometimes reflect the way in which users think about data. Users may prefer to generate queries using a star schema in some cases, although this may or may not be reflected in the underlying organization of the database.
* Some users may wish to submit queries to the database which, using conventional multidimensional reporting tools, cannot be expressed within a simple star schema. This is particularly common in data mining of customer databases, where a common requirement is to locate common factors between customers who bought products meeting complex criteria. Some snowflaking would typically be required to permit simple query tools to form such a query, especially if provision for these forms of query weren't anticipated when the data warehouse was first designed.