contain a potentially different billing address and be indexed by an accounting "Customer Account Number". In both instances the customer name is the same, but is identified and stored differently. Deriving any correlation between data extracted from those two databases presents a challenge. In contrast, a data warehouse is organized around subjects. Subject orientation presents the data in a format that is consistent and much clearer for end users to understand. For example subjects could be "Product", "Customers", "Orders" as opposed to "Purchasing", "Payroll". Integrated Integration of data within a warehouse is accomplished by dictating consistency in format, naming, etc. Operational databases, for historic reasons, often have major inconsistencies in data representation. For example, a set of operational databases may represent "male" and "female" by "m" and "f", by "1" and "2", by "x" and "y". Frequently the inconsistencies are more complex and subtle. By definition, data is always maintained in a consistent fashion in a data warehouse. Time variant Data warehouses are time variant in the sense that they maintain both historical and (nearly) current data. Operational databases, in contrast, contain only the most current, up-to-date data values. Furthermore, they generally maintain this information for no more than a year (and often much less). By comparison, data warehouses contain data that is generally loaded from the operational databases daily, weekly, or monthly and then typically maintained for a period of 3 to 5 years. This aspect marks a major difference between the two types of environments. Historical information is of high importance to decision-makers. They often want to understand trends and relationships between data. For example, the product manager for a soft drink maker may want to see the relationship between coupon promotions and sales. This type of information is typically impossible to determine with an opera...
