Organized by business dimension or subject: Data are organized by subject-for example, by the customer, vendor, product, price level, and region. This arrangement differs from transactional systems, where data are organized by business process, such as order entry, inventory control, and accounts receivable.

Use online analytical processing: Typically, organizational databases are oriented toward handling transactions. That is, databases use online transaction processing (OLTP), where business transactions are processed online as soon as they occur. The objectives are speed and efficiency, which are critical to a successful Internet-based business operation. Data warehouses and data marts, which are designed to support decision-makers but not OLTP, use online analytical processing. Online analytical processing (OLAP) involves the analysis of accumulated data by end-users.

Integrated: Data are collected from multiple systems and then integrated around subjects. For example, customer data may be extracted from internal (and external) systems and then integrated around a customer identifier, thereby creating a comprehensive view of the customer.

Time variant: Data warehouses and data marts maintain historical data (i.e., data that include time as a variable). Unlike transactional systems, which maintain only recent data (such as for the last day, week, or month), a warehouse or mart may store years of data. Organizations utilize historical data to detect deviations, trends, and long-term relationships.

Nonvolatile: Data warehouses and data marts are nonvolatile-that is, users cannot change or update the data. Therefore the warehouse or mart reflects the history, which, as we just saw, is critical for identifying and analyzing trends. Warehouses and marts are updated, but through IT-controlled load processes rather than by users.

Multidimensional: Typically the data warehouse or mart uses a multidimensional data structure. Recall that relational databases store data in two-dimensional tables. In contrast, data warehouses and marts store data in more than two dimensions. For this reason, the data are said to be stored in a multidimensional structure. A common representation of this multidimensional structure is the data cube.