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The focus eventually turned to smaller data warehouses focused on specific areas of a business that could most benefit from in-depth data analysis. The term data mart was coined to describe these smaller (but still often massive) data warehouses. With the advent of multiple data marts within enterprises, a recent area of focus has been on management of distributed data marts. In particular, there is a large potential for duplication of effort in the data cleansing and reformatting process when multiple marts are drawing data from the same production databases. One emerging answer seems to be a coordinated approach to data transformation for distributed marts, rather than a return to huge centralized warehouses. Another approach is to leave the data in place in OLTP databases and form data marts on demand through use of a middleware tool that makes the data in multiple databases appear as if it is all in one huge, federated database, which can be thought of as a virtual data warehouse. In this architecture, known as enterprise information integration (EII), the middleware tool replicates each query across all the supported physical databases and consolidates the results before returning them to the user who submitted the original query. Data warehousing, and more recently data marts, have grown to prominence in many different industries. They are most widely (and aggressively) used in industries where better information about business trends can be used to make decisions that save or generate large amounts of money. For example: High-volume manufacturing Analysis of customer purchase trends, seasonality, and so on, can help the company plan its production and lower its inventory levels, saving money for other purposes. Packaged goods Analysis of promotions (coupons, advertising campaigns, direct mail, etc.) and the response of consumers with different demographics can help to determine the most effective way to reach prospective customers, saving millions of dollars in advertising and promotion costs. Telecommunications Analysis of customer calling patterns can help to create more attractive pricing and promotional plans, perhaps attracting new customers from a competitor. Airlines Analysis of customer travel patterns is critical to yield management, the process of setting airfares and associated restrictions on available airline seats to maximize profitability. Financial services Analysis of customer credit factors and comparing them with historical customer profiles can help to make better decisions about which customers are creditworthy.
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The structure (schema) of a warehouse database is typically designed to make the information easy to analyze, since that is the major focus of its use. The structure must make it easy to slice and dice the data along various dimensions. For example, one day a business analyst may want to look at sales by product category by region, to compare the performance of different products in different areas of the country. The next day, the same analyst may want to look at sales trends over time by region, to see which regions are growing and which are not. The structure of the database must lend itself to this type of analysis along several different dimensions.
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FIGURE 21-2 Three-dimensional depiction of sales data
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Shoes Accessories Linens Clothing JAN FEB MAR APR MAY JUN $50,475 $55,607 $61,977 $55,403 $62,673 $65,973 East $67,463 $65,345 $64,730 $63,400 $62,478 $61,995 West $89,475 $93,143 $94,006 $97,105 $97,847 $98,567 Central
Fact Cubes
In most cases, the data stored in a warehouse can be accurately modeled as an N-dimensional cube (N-cube) of historical business facts. A simple three-dimensional (3-D) cube of sales data is shown in Figure 21-2 to illustrate the structure. The fact in each cell of the cube is a dollar sales amount. Along one edge of the cube, one of the dimensions is the month during which the sales took place. Another dimension is the region where the sales occurred. The third dimension is the type of product that was sold. Each cell in the cube represents the sales for one combination of these three dimensions. The $50,475 amount in the upper-left front cell represents the sales amount for January, for clothing, in the East region. Figure 21-2 shows a simple 3-D cube, but many warehousing applications will have a dozen dimensions or more. Although a 12-D cube is difficult to visualize, the principles are the same as for the 3-D example. Each dimension represents some variable on which the data may be analyzed. Each combination of dimension values has one associated fact value, which is usually the historical business result obtained for that collection of dimension values. To illustrate the database structures typically used in warehousing applications, we use a warehouse that might be found in a distribution company. Through the efforts of its sales force, the company distributes different types of products, made by various suppliers, to several hundred customers located in various regions of the country. The company wants to analyze historical sales data along these dimensions, to discover trends and gain insights that will help it better manage its business. The underlying model for this analysis will be a 5-D fact cube with these dimensions: Category The category of product that was sold, with values such as clothing, linens, accessories, and shoes. The warehouse has about two dozen product categories. Supplier The supplier who manufactures the particular product sold. The company might distribute products from 50 different suppliers. Customer The customer who purchased the products. The company has several hundred customers. Some of the larger customers purchase products centrally and are serviced by a single salesperson; others purchase on a local basis and are served by local salespeople.
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