Using OLAP to Answer Business Questions OLAP

Relational databases provide the online transactional processing (OLTP) that is essential for businesses to keep track of their affairs. Designed for efficient selection, storage, and retrieval of data, relational databases are ideal for housing gigabytes of detailed data.
The success of relational databases is apparent in their use to store information about an increasingly wide scope of activities. As a result, they contain a wealth of data that can yield critical information about a business. This information can provide a significant edge in an increasingly competitive marketplace.
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.
A standard transactional query might ask, "When did order 84305 ship?" This query reflects the basic mechanics of doing business. It involves simple data selection and retrieval of one record (or, at most, several related records) identified by a unique order number. Any follow-up questions, such as which postal carrier was used and Common Analytical Applications where was the order shipped to, can probably be answered by the same record. 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. At this point, the record can be rolled off to an archive.
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?"
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. The analytic database is a "crystal ball" for decision makers whose ability to make sound decisions today is dependent on how well they can predict the future. Getting the answers to these questions involves single-row calculations, time series analysis, and access to aggregated historical and current data. This requires OLAP -- online analytical processing.


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