A Data Warehouse is a specific database designed and populated to support decision-making in an organization. It is batch updated and structured for rapid online queries and managerial summaries. Data warehouses contain large amounts of data – 500 megabytes and more. According to data warehousing pioneer Bill Inmon, "A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management's decision making process". Information systems specialists should read Inmon’s paper titled "What is a Data Warehouse" on the World Wide Web.
What does Inmon mean by his four characteristics of a data warehouse? Subject-oriented means it focuses on subjects related to business or organizational activity like customers, employees and suppliers. Integrated means the data is stored in a consistent format through use of naming conventions, domain constraints, physical attributes and measurements. Time-variant refers to associating data with specific points in time. Finally, non-volatile means the data does not change once it is in the warehouse and stored for decision support. Ralph Kimball (1996), another data warehousing pioneer, states that "a data warehouse is a copy of transaction data specifically structured for query and analysis".
A related term is Data Mart. A data mart is a more focused or a single subject data warehouse. For example, some companies build a customer data mart rather than a multi-subject data warehouse. It would have all of the information about customers. Many organizations and businesses are starting their enterprise-wide data warehouses by building a series of focused data marts. Some commentators have combined and confused data warehousing and online analytical processing systems. We should recognize them as different sub-categories of Data-Driven DSS.
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