12 Mining Your Own Business in Banking Using DB2 Intelligent Miner for Data
Data preparation for mining is usually a very time consuming task, often the
mining itself requires less effort. The optimal way to do data preprocessing for
data mining is typically very dependent on the technology used and the current
skills, the volume of data to be processed and the frequency of updates.
Figure 2-1 shows where datamarts are located logically within the BI
A datamart contains data from the data warehouse tailored to support the
specific requirements of a given business unit, business function or application.
The main purpose of a data mart can be defined as follows:
To store pre-aggregated information
To control end user access to the information
To provide fast access to information for specific analytical needs or user
To represent the end users view and data interface of the data warehouse
To create the multidimensional/relational view of the data
The database format can either be multidimensional or relational.
When building datamarts it is important to keep the following in mind:
Data marts should always be implemented as an extension of the data
warehouse, not as an alternative. All data residing in the data mart should
therefore also reside in the data warehouse. In this way the consistency and
reuse of data is optimized.
Data marts are typically constructed to fit one requirement, ideally. However,
you should be aware of the trade-off between the simplicity of design (and
performance benefits) compared to the cost of administrating and maintaining
a large number of data marts.
The metadata structures the information in the data warehouse in categories,
topics, groups, hierarchies and so on. They are used to provide information about
the data within a data warehouse, as given in the following list (also see
Metadata are “subject oriented” and are based on abstractions of real-world
entities, for example, “project”, “customer”, or “organization”.