The core challenges associated with enterprise OLAP are how to structure the database and how to build a decision support architecture that allows for complex analysis and qualification. The primary components of an OLAP system-the database and the analysis engine-must be capable of handling large amounts of data in varying structures. Warehouses will continue to grow and contain data that is difficult if not impossible to store in fixed structures such as a star schema. In parallel, analysis requirements will continue to increase in complexity in terms of calculation, and qualification. OLAP tools must remove data size limitations and data structure requirements while providing a means of delivering advanced analytical functionality even for large data volumes. The key to overcoming these challenges lies in achieving tighter integration between analysis tools and databases. The database and the analytical engine must not impose restrictions on each other such that the ability to manage or analyze information in the warehouse is compromised.
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