Although the data warehousing process prepares data for end user consumption, most information in a relational data warehouse is not easily browsed.
Data structures are often difficult for the end user to comprehend and questions such as “Who are the top sales people in each region for the last year by month?” are complex when expressed in SQL. Some of these challenges can be addressed with advanced query tools, which hide the database complexity from the end user, but for the larger class of applications in which the end user is viewing multidimensional data, Microsoft believes the optimal solution is OLAP technology.
All organizations, regardless of size, must manage complex multidimensional data. Even the smallest organization may need to track sales by product, salesperson, geography, customer, and time. Organizations have sought tools to access, navigate, and analyze multidimensional data in an easy, natural way.
OLAP is not a new concept, but the OLAP name has been given to this technology only recently. In 1993, Dr. E. F. Codd, the database researcher and inventor of the relational database model, coined the term in his paper, “Providing OLAP to User Analysis: An IT Mandate,” wherein he laid out 12 rules that defined the characteristics of OLAP applications. Nigel Pendse and Richard Creeth of the OLAP Report (www.olapreport.com/fasmi.htm) later refined his definition with what is called the FASMI test. This test states that OLAP applications should deliver fast analysis of shared multidimensional information following these guidelines: