They generate new data using predictive analytical tools such as models, forecasts, aggregation, allocation, and scenario management. Planning applications enable organizations to predict outcomes. Here are a few examples of common applications that can use the OLAP option to realize valuable gains in functionality and performance: This requires OLAP - online analytical processing. Getting the answers to these questions involves single-row calculations, time series analysis, and access to aggregated historical and current data. The analytic database provides the information needed by decision makers whose ability to set goals today is dependent on how well they can predict the future. These are not questions about doing business transactions, but about analyzing past performance and making decisions that will improve future performance, provide a more competitive edge, and thus enhance profitability. In contrast, a typical series of analytical queries might ask, "How do sales in the Pacific Rim for this quarter compare with sales a year ago? What can we predict for sales next quarter? What factors can we alter to improve the sales forecast? What happens if I change this number?" ![]() At this point, the record can be rolled off to an archive. This record has a useful life span in the transactional world: it begins when a customer places the order and ends when the order is shipped and paid for. Any follow-up questions, such as which postal carrier was used and where was the order shipped to, can probably be answered by the same record. It involves simple data selection and retrieval of one record (or, at most, several related records) identified by a unique order number. The challenge is in deriving answers to business questions from the available data, so that decision makers at all levels can respond quickly to changes in the business climate.Ī standard transactional query might ask, "When did order 84305 ship?" This query reflects the basic mechanics of doing business. This information can provide a significant edge in an increasingly competitive marketplace. As a result, they contain a wealth of data that can yield critical information about a business. ![]() The success of relational databases is apparent in their use to store information about an increasingly wide scope of activities. Designed for efficient selection, storage, and retrieval of data, relational databases are ideal for housing gigabytes of detailed data. Relational databases provide the online transactional processing (OLTP) that is essential for businesses to keep track of their affairs. Standard reporting applications can present the results of complex multidimensional calculations, while ad-hoc querying tools such as custom aggregate members and custom measures can expand the analyst's range of calculation functions. OLAP calculations can be queried using SQL, enabling application developers to leverage their investment in SQL while expanding the analytic sophistication of their software to include modeling, forecasting, and what-if analysis. SQL-based applications can now use pure SQL against information-rich relational views of multidimensional data provided by an OLAP-enabled Oracle Database. ![]() A single application can access both relational and multidimensional data. The DBA can decide the best location for storing and calculating the data as part of optimizing the operations of the database. ![]() DBAs use the same tools to administer this option as they use to administer all other components of the database. In contrast, the OLAP option is fully integrated into the Oracle Database. Full Integration of Multidimensional Technology
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