The need of end users to analyze corporate data for the purpose of making better decisions is of paramount importance.

OLAP technologies are essential to delivering this end-user value and are a critical component of a broader information technology architecture.

Corporate data needs to be transformed into multidimensional aggregations supporting enterprise-scale data volumes and hierarchies.

Fast, consistent response to end-user requests is critical to interactive, ad-hoc exploration, comparison and analysis of data, regardless of database size and complexity.

End users must be able to manipulate and derive data for analysis purposes by applying analytical operations such as ratios, cumulative totals, trends and allocations across dimensions and across hierarchical levels.

An OLAP Server may either physically stage the processed multidimensional information or populate its data structures in real-time from relational, OLTP or other databases, as long as it delivers consistent and rapid response to end users.

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