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

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

created on-site or bought from another party. They should be selected and

filtered from redundant information.)

򐂰 Evaluating the data quality.

򐂰 Choosing the mining function and defining the mining run.

򐂰 Interpreting the results and detecting new information.

򐂰 Deploying the results and the new knowledge into your business.

These steps are illustrated in Figure 3-4 and the following sections expand on

each of the stages.

Figure 3-4 The generic method

3.4.1 Step 1 — Defining the business issue

All to often, organizations approach data mining from the perspective that there

must be some value in the data we have collected, so we will just use data mining

and discover what’s there. Using the mining analogy, this is rather like choosing a

spot at random and starting to dig for gold. This may be a good strategy if the

gold is in large nuggets, and you were lucky enough to choose the right spot to

Define the

data

Step 1: The business issue

Step 2: Using a common data model

or how to define it when it does

not exist

Step 3: Sourcing and preparing the data

Step 4: Evaluating the data

Step 5: Mining the data

Step 6: Interpreting the results

Step 7: Deploying the results

Define the

Business

issue

Choose the

Mining

technique

Interpret the

results

Deploy the

results

Evaluate the

Data model

Source the

Data

Date Model to

use

Define the

data

Chapter 3. A generic data mining method 31

dig, but if the gold could only be extracted by “panning” or some other technique,

you may spend a lot of time throwing away the valuable material in a fruitless

search, for searching for the right thing at the wrong place or with the wrong

technique.

Data mining is about choosing the right tools for the job and then using them

skillfully to discover the information in your data. We have already seen there are

a number of tools that can be used, and that very often we have to use a

combination of the tools at our disposal, if we are to make real discoveries and

extract the value from our data.

The

first step in our data mining method is therefore to identify the business

issue that you want to address and then determine how the business issue can

be translated into a question, or set of questions, that data mining can address.

By

business issue we mean that there is an identified problem to which you need

an answer, where you suspect, or know, that the answer is buried somewhere in

the data, but you are not sure where it is.

A business issue should fulfill the requirements of having:

򐂰 A clear description of the problem to be addressed

򐂰 An understanding of the data that might be relevant

򐂰 A vision for how you are going use the mining results in your business

Describing the problem

If you are not sure what questions data mining can address, then the best

approach is to look at examples of where it has been successfully used, either in

your own industry or in related industries. Many business and research fields

have been proven to be excellent candidates for data mining. The major fraction

are covered by banking, insurance, retail and telecommunications (telecoms),

but there are many others such as manufacturing, pharmaceuticals,

biotechnology and so on, where significant benefits have also been derived.

Well-known approaches are: customer profiling and cross-selling in retail, loan

delinquency and fraud detection in banking and finance, customer retention

(attrition and churn) in telecoms, patient profiling and weight rating for Diagnosis

Related Groups in health care and so on. Some of these are depicted in

Figure 3-5.