Large amounts of time and money are invested in building data warehouses in the hope that the enterprise will get the information it needs to make strategic decisions of lasting value. For maximizing the value potential, you need to cater to as large a user group as possible to tap into the potential of the warehouse. This includes the extension of OLAP capabilities to a larger group of analysts.
The early warehouses started as small-scale decision support systems for a selected handful of interested analysts. Early mainframe decision support systems provided powerful analytical capabilities although quite incomparable to today's OLAP systems. Because those systems were difficult to use, they seldom reached beyond a small group of analysts who could plough through the difficulties.
The next generation of decision support systems replaced complex mainframe computing with easy-to-use GUIs and point-and-click interfaces. These second-generation systems running on client/server architecture were gradually able to support OLAP in addition to simple querying and reporting. Still, deployment and maintenance costs prevented extension of decision support to larger number of users. OLAP and OLAP-like capabilities were still limited to a small number of users.
The Web has put a dramatically different slant on information delivery. Web-enabled data warehouses can open their doors to a large group of users both within and outside the enterprise, ...