This data comes from transactions with customers. It comes from events created during internal business processes. It comes from informal data not maintained in centrally managed databases, but which is nonetheless integral to comprehensive analysis. The data explosion is compounded each year as new, disparate collection systems are deployed. Companies overwhelmed by the information tidal wave are increasingly deploying multi-dimensional analysis techniques.
Analysts naturally view the business in a multi-dimensional manner.
For example, an analyst may need to look at revenue for sales to customers, by product, by region, over time. OLAP provides a framework to organize data in a model that resembles the analyst's understanding of the business. This enables the analyst to understand, navigate and manipulate the information easily and more intuitively than they can with traditional relationship databases. An OLAP server is ideally suited to handle complex, cross-dimensional calculations, which is important to the analyst relying on derived data. This allows for storage of pre-calculated data at numerous granular and hierarchical intersections, ensuring quick query response times.