PDF of this tutorialOne of the technology areas that will be critical to supporting the business model discussed in Topic 4 is data warehousing.
Data may be viewed and analyzed from the warehouse in a number of ways. One very common retrieval paradigm involves the use of an on-line analytical processing (OLAP) engine. OLAP is the term most often used to describe the broad range of analysis for which the data warehouse is used. It is different from on-line transaction processing (OLTP) in a variety of ways, but the key difference is that OLAP focuses on processing queries to the database, whereas OLTP systems process transactions in which information is entered into the database. OLTP applications are rarely, if ever, built on data warehouses.
OLAP products can offer a range of advanced analysis capabilities, such as Top N, data pivoting for multidimensional analysis, statistical functionality, the ability to drill from high-level data to successive levels of detail for iterative analysis, etc. OLAP engines can be leveraged to deliver high performance and extended analytics for ad hoc query and reporting tools, as well as for specialized packaged applications or custom executive information systems (EIS).
Data warehouses can also be used to support sophisticated operational analysis functions such as customer scoring systems (described earlier) and fraud detection applications. All of these facilities may be used in concert with a separate process known as data mining. Data mining is the practice of polling data for interesting elements or anomalies without actually having to pose specific questions or queries. Data mining is very broadly defined in the industry, and consequently, data mining software can vary greatly in terms of functionality and scope. It usually references sampled data and is often customized to an individual organization’s needs.
Figure 3. Simplified Data Warehouse Architecture
