Промышленный лизинг Промышленный лизинг  Методички 

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important type of data mart is an exploratory environment used for data mining, which is discussed in the next chapter.

Not all the data in data marts needs to come from the central repository. Often specific applications have an exclusive need for data. The real estate department, for instance, might be using geographic information in combination with data from the central repository. The marketing department might be combining zip code demographics with customer data from the central repository. The central repository only needs to contain data that is likely to be shared among different applications, so it is just one data source-usually the dominant one-for data marts.

Operational Feedback

Operational feedback systems integrate data-driven decisions back into the operational systems. For instance, a large bank may develop cross-sell models to determine what product next to offer a customer. This is a result of a data mining system. However, to be useful this information needs to go back into the operational systems. This requires a connection back from the decision-support infrastructure into the operational infrastructure.

Operational feedback offers the capability to complete the virtuous cycle of data mining very quickly. Once a feedback system is set up, intervention is only needed for monitoring and improving it-letting computers do what they do best (repetitive tasks) and letting people do what they do best (spot interesting patterns and come up with ideas). One of the advantages of Web-based businesses is that they can, in theory, provide such feedback to their operational systems in a fully automated way.

End Users and Desktop Tools

The end users are the final and most important component in any data warehouse. A system that has no users is not worth building. These end users are analysts looking for information, application developers, and business users who act on the information.

Analysts

Analysts want to access as much data as possible to discern patterns and create ad hoc reports. They use special-purpose tools, such as statistics packages, data mining tools, and spreadsheets. Often, analysts are considered to be the primary audience for data warehouses.

Usually, though, there are just a few technically sophisticated people who fall into this category. Although the work that they do is important, it is difficult to justify a large investment based on increases in their productivity. The virtuous cycle of data mining comes into play here. A data warehouse brings


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together data in a cleansed, meaningful format. The purpose, though, is to spur creativity, a very hard concept to measure.

Analysts have very specific demands on a data warehouse:

The system has to be responsive. Too much of the work of analysis is in the form of answering urgent questions in the form of ad hoc analysis or ad hoc queries.

Data needs to be consistent across the database. That is, if a customer started on a particular date, then the first occurrence of a product, channel, and so on should be exactly on that date.

Data needs to be consistent across time. A field that has a particular meaning now should have the same meaning going back in time. At the very least, differences should be well documented.

It must be possible to drill down to customer level and preferably to the transaction level detail to verify values in the data warehouse and to develop new summaries of customer behavior.

Analysts place a heavy load on data warehouses, and need access to consistent information in a timely manner.

Application Developers

Data warehouses usually support a wide range of applications (in other words, data marts come in many flavors). In order to develop stable and robust applications, developers have some specific needs from the data warehouse.

First, the applications they are developing need to be shielded from changes in the structure of the data warehouse. New tables, new fields, and reorganizing the structure of existing tables should have a minimal impact on existing applications. Special application-specific views on the data help provide this assurance. In addition, open communication and knowledge about what applications use which attributes and entities can prevent development gridlock.

Second, the developers need access to valid field values and to know what the values mean. This is the purpose of the metadata repository, which provides documentation on the structure of the data. By setting up the application to verify data values against expected values in the metadata, developers can circumvent problems that often appear only after applications have rolled out.

The developers also need to provide feedback on the structure of the data warehouse. This is one of the principle means of improving the warehouse, by identifying new data that needs to be included in the warehouse and by fixing problems with data already loaded. Since real business needs drive the development of applications, understanding the needs of developers is important to ensure that a data warehouse contains the data it needs to deliver business value.



The data warehouse is going to change and applications are going to continue to use it. The key to delivering success is controlling and managing the changes. The applications are for the end users. The data warehouse is there to support their data needs-not vice versa.

Business Users

Business users are the ultimate devourers of information derived from the corporate data warehouse. Their needs drive the development of applications, the architecture of the warehouse, the data it contains, and the priorities for implementation.

Many business users only experience the warehouse through printed reports, static online reports, or spreadsheets-basically the same way they have been gathering information for a long time. Even these users will experience the power of having a data warehouse as reports become more accurate, more consistent, and easier to produce.

More important, though, are the people who use the computers on their desks and are willing to take advantage of direct access to the data warehousing environment. Typically, these users access intermediate data marts to satisfy the vast majority of their information needs using friendly, graphical tools that run in their familiar desktop environment. These tools include off-the-shelf query generators, custom applications, OLAP interfaces, and report generation tools. On occasion, business users may drill down into the central repository to explore particularly interesting things they find in the data. More often, they will contact an analyst and have him or her do the heavier analytic work.

Business users also have applications built for specific purposes. These applications may even incorporate some of the data mining techniques discussed in previous chapters. For instance, a resource scheduling application might include an engine that optimizes the schedule using genetic algorithms. A sales forecasting application may have built-in survival analysis models. When embedded in an application, the data mining algorithms are usually quite well hidden from the end user, who cares more about the results than the algorithms that produced them.

Where Does OLAP Fit In?

The business world has been generating automated reports to meet business needs for many decades. Figure 15.4 shows a range of common reporting



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