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.
2.2.5 Datamarts
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.
2.2.6 Metadata
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
Figure 2-2):
򐂰 Metadata are subject oriented and are based on abstractions of real-world
entities, for example, project, customer, or organization.
Chapter 2. Business Intelligence architecture overview 13
򐂰 Metadata define the way in which the transformed data is to be interpreted,
for example, 5/9/99 = 5th September 1999 or 9th May 1999 British or
򐂰 Metadata give information about related data in the data warehouse.
򐂰 Metadata estimate response time by showing the number of records to be
processed in a query.
򐂰 Metadata hold calculated fields and pre-calculated formulas to avoid
misinterpretation, and contain historical changes of a view.
Figure 2-2 Metadata with a central role in BI
The data warehouse administrators perspective of metadata is a full
repository and documentation of all contents and processes within the data
warehouse; from an end user perspective, metadata is the roadmap through
the information in the data warehouse.
Technical versus business metadata
Metadata users can be broadly placed into the categories of business users and
technical users. Both of these groups contain a wide variety of users of the data
warehouse metadata. They all need metadata to identify and effectively use the
information in the data warehouse.
Therefore, we can distinguish between two types of metadata that the repository
will contain technical and business metadata:

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