数据仓-从你的数据仓库发掘隐藏财富(doc14)英文版-物料管理(编辑修改稿)

数据仓-从你的数据仓库发掘隐藏财富(doc14)英文版-物料管理(编辑修改稿)内容摘要：

n the past. Hopefully, if you39。 ve got a good model, you find your treasure. This act of model building is thus something that people have been doing for a long time, certainly before the advent of puters or data mining technology. What happens on puters, however, is not much different than the way people build models. Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the puter must run through that data and distill the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don39。 t know the answer. For example, say that you are the director of marketing for a telemunications pany and you39。 d like to acquire some new long distance phone customers. You could just randomly go out and mail coupons to the general population just as you could randomly sail the seas looking for sunken treasure. In neither case would you achieve the results you desired and of course you have the opportunity to do much better than random you could use your business experience stored in your database to build a model. As the marketing director you have access to a lot of information about all of your customers: their age, sex, credit history and long distance calling usage. The good news is that you also have a lot of information about your prospective customers: their age, sex, credit history etc. Your problem is that you don39。 t know the long distance calling usage of these prospects (since they are most likely now customers of your petition). You39。 d like to concentrate on those prospects who have large amounts of long distance usage. You can acplish this by building a model. Table 2 illustrates the data used for building a model for new customer prospecting in a data warehouse. Customers Prospects General information (. demographic data) Known Known Proprietary information (. customer transactions) Known Target Table 2 Data Mining for Prospecting The goal in prospecting is to make some calculated guesses about the information in the lower right hand quadrant based on the model that we build going from Customer General Information to Customer Proprietary Information. For instance, a simple model for a telemunications pany might be: 98% of my customers who make more than $60,000/year spend more than $80/month on long distance This model could then be applied to the prospect data to try to tell something about the proprietary information that this telemunications pany does not currently have access to. With this model in hand new customers can be selectively targeted. Test marketing is an excellent source of data for this kind of modeling. Mining the results of a test market representing a broad but relatively small sample of prospects can provide a foundation for identifying good prospects in the overall market. Table 3 shows another mon scenario for building models: predict what is going to happen in the future. Yesterday Today Tomorrow Static information and current plans (. demographic data, marketing plans) Known Known Known Dynamic information (. customer transactions) Known Known Target Table 3 Data Mining for Predictions If someone told you that he had a model that could predict customer usage how would you know if he really had a good model? The first thing you might try would be to ask him to apply his model to your customer base where you already knew the answer. With data mining, the best way to acplish this is by setting aside some of your data in a vault to isolate it from the mining process. Once the mining is plete, the results can be tested against the data held in the vault to confirm the model’s validity. If the model works, its observations should hold for the vaulted data. An Architecture for Data Mining To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the anization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on. Figure 1 illustrates an architecture for advanced analysis in a large data warehouse. Figure 1 Integrated Data Mining Architecture The ideal starting point is a data warehouse containing a bination of internal data tracking all customer contact coupled with external market data about petitor activity. Background information on potential customers also provides an excellent basis for prospecting. This warehouse can be implemented in a variety of relational database systems: Sybase, Oracle, Redbrick, and so on, and should be optimized for flexible and fast data access. An OLAP (OnLine Analytical Processing) server enables a more sophisticated enduser business model to be applied when navigating the data warehouse. The multidimensional structures allow the user to analyze the data as they want to view their business – summarizing by product line, region, and other key perspectives of their business. The Data Mining Server must be integrated with the data warehouse and the OLAP server。