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A data warehouse (DW) is a collection of corporate information and data derived from operational systems and external data sources. A data warehouse is designed to support business decisions by allowing data consolidation, analysis and reporting at different aggregate levels. Data is populated into the DW through the processes of extraction, transformation and loading.
The data warehouse architecture was born in the 1980s as an architectural model designed to support the flow of data from operational systems to decision support systems. These systems require analysis of large amounts of heterogeneous data accumulated by companies over time.
In a data warehouse, data from many heterogeneous sources is extracted into a single area, transformed according to the decision support system needs and stored into the warehouse. For example, a company stores information pertaining to its employees, their salaries, developed products, customer information, sales and invoices. The CEO might want to ask a question pertaining to the latest cost-reduction measures; the answers will involve analysis of all of this data. This is a main service of the data warehouse, i.e., allowing executives to reach business decisions based on all these disparate raw data items.
Thus, a data warehouse contributes to future decision making. As in the above example, a firm administrator can query warehouse data to find out the market demand of a particular product, sales data by geographical region or answers other inquiries. This provides insight about required steps to more effectively market a particular product. Unlike an operational data store, a data warehouse contains aggregate historical data, which may be analyzed to reach critical business decisions. Despite associated costs and effort, most major corporations today use data warehouses.