Today, enterprises have adopted the technique of data warehousing to organize and secure data so it is available to those who require ready access to it. Properly implemented, data warehouses allow businesses to cope with narrowing market windows, shortened product cycles, demands for greater profitability, and the need to differentiate customer service. Most data warehouses are hundreds of gigabytes in size, with many nearing a terabyte. Some prognosticators foresee a day when terabyte data warehouses will be  the norm, with capacities of hundreds of  terabytes not uncommon. With the explosion of corporate databases and the stockpiling of more and more kinds of business data, corporations worldwide are turning to parallel processing technologies such as those available from Sun.
Several technologies are available for extracting information from data warehouses.
Many analysts begin by making educated guesses about relationships in the  data and creating simple database queries to confirm or deny their suspicions. One assumption common to all these approaches is that the individuals sifting through the data know what they are looking for. Most OLAP tools organize their data in predefined ways, which can have the effect of prejudging the data. What troubles analysts today is that there are hidden relationships no one has thought to ask about. All agree that in order to get the maximum benefit from data warehouses, a tool is needed to reveal the nonobvious.