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
kind 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.
Copyright © 2009 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
