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Analytical Processing for Business Decisions High-level analytical queries, like those commonly used in Siebel Analytics, scan and analyze large volumes of data using complex formulas. This process can take a long time when querying an OLTP database, impacting overall system performance. Because complex queries run slowly on OLTP databases, the database requirements for Siebel Analytics are different from other parts of Siebel operational applications. In Siebel Analytics, you will modify data much less frequently than in Siebel operational applications, but you will need quick results when viewing new analyses, drilling down to detailed charts and graphs, and creating new briefings. To address these requirements, you need a physical implementation of the data model that is optimized for quick review of the entire database of information rather than quick updating of that information. Such a database will have as few join paths as possible to minimize processing. This means fewer, larger database tables rather than many smaller ones. In such a database schema, the same piece of data may appear in several locations, which reduces the need for join paths. This type of database is called denormalized. The Siebel Data Warehouse is an online analytical processing (OLAP) database, which allows you to selectively extract, analyze, and view data. The Siebel Data Warehouse schema was designed using star schema modeling techniques (called dimensional schema in this book) to support the analysis requirements of Siebel Analytics. To facilitate this kind of analysis, Siebel Data Warehouse data is stored in a relational database that considers each data attribute (such as product, account, and time period) as a separate dimension. The Siebel Data Warehouse does not contain every piece of data stored in a transactional database, because not all transactional data is needed for analysis.