Online analytical processing (OLAP) systems--which have their roots in decision support systems (DSSs) and executive information systems (EISs)--store data in multidimensional databases. You then access the databases to perform financial and statistical analyses on different combinations of the data. OLAP products are particularly useful for time-series analyses and recursive calculations.

Vendors offer a variety of OLAP products that you can group into three categories: relational OLAP (ROLAP), multidimensional OLAP (MOLAP), and hybrid OLAP (HOLAP). ROLAP products (e.g., Informix's MetaCube ROLAP Option for the Informix Dynamic Server, MicroStrategy's DSS Agent) adapt traditional relational databases to support OLAP. Vendors often use a star schema structure to extend and adapt an underlying relational database as an OLAP server.

MOLAP products (such as Arbor Software's Arbor Essbase OLAP Server 5 and Oracle's Oracle Express Server) provide multi-dimensional analyses of data by putting data in a cube structure. Most successful MOLAP products use a multicube approach in which a series of small, dense, precalculated cubes make up a hypercube.

HOLAP products (e.g., Microsoft SQL Server OLAP Services and Pilot Software's Pilot Decision Support Suite) combine MOLAP and ROLAP. With HOLAP products, a relational database stores most of the data. A separate multi-dimensional database stores the most dense data, which is typically a small proportion of the data.