Mining Your Own Business in Telecoms Using DB2 Intelligent Miner for Data

12 Mining Your Own Business in Telecoms 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

architecture.

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

group

򐂰 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 data marts, 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

US?

򐂰 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 administrator’s 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: