Organizations usually have databases and data stores
that maintain repeated and frequent business transaction data. This
provides simple yet detailed storage and retrieval of specific data
events. However, these data storage systems are not well suited for
analytical summaries and queries that are typically generated by decision
makers. For decision makers to reveal hidden trends, inconsistencies,
and risks in a business, they must be able to maintain a certain degree
of momentum when querying the data. An answer to one question usually
leads to additional questions and review of the data. Simple data
stores do not generally suffice.
The data warehouse is a structure better
suited for this type of querying. In a data warehouse, data is maintained
and organized so that complicated queries and summaries can be run.
OLAP further organizes and summarizes specific categories and subsets
of data from the data warehouse. One particular type of data structure
derived from a data warehouse is the cube. A cube is a set of data
that is organized and structured in a hierarchical, multidimensional
arrangement. Such an arrangement results in a robust and detailed
level of data storage with efficient and fast query returns. Stored, precalculated summarizations called aggregations
can be added to the cube to improve cube access performance.