Database Reference
In-Depth Information
CHAPTER FOUR
The Mining Data Mart
DESIGNING THE MINING DATA MART
The success of a data mining project strongly depends on the breadth and quality
of the available data. That is why the data preparation phase is typically the most
time-consuming phase of the project. In the following sections we will deal with
the issues of selecting, preparing, and organizing data for data mining purposes.
Data mining applications should not be considered as one-off projects but
rather as continuous processes, integrated into the organization's marketing strat-
egy. Data mining has to be ''operationalized.'' Derived results should be made
available to marketers to guide them in their everyday marketing activities. The
results should also be loaded into the organization's front-line systems in order to
enable ''personalized'' customer handling. This approach requires the setting up of
well-organized data mining procedures, designed to serve specific business goals,
instead of occasional attempts which just aim to cover sporadic needs.
In order to achieve this and become a ''predictive enterprise'' an organization
should focus on the data to be mined. Since the goal is to turn data into actionable
knowledge, a vital step in this ''mining quest'' is to build the appropriate data
infrastructure. Ad hoc data extraction and queries which just provide answers to
a particular business problem may soon end up as a huge mess of unstructured
information. The proposed approach is to design and build a central mining data
mart that will serve as the main data repository for the majority of the data mining
applications. All relevant information should be taken into account in the design of
the data mart. Useful information from all available data sources, including internal
sources such as transactional, billing, and operational systems, and external sources
such as market surveys and third-party lists, should be collected and consolidated
in the data mart framework. After all, this is the main idea of the data mart: to
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