Before going into further detail, it is important to understand the various stages of the data mining life cycle. The data mining life cycle can be broadly classified into the following steps:

Let's look at each of these stages in detail.

First and foremost, the task that needs to be well defined even before beginning the mining process is to identify the goals. This is a crucial part of the data mining exercise and you need to understand the following questions:

Let's define a classic problem and understand more about the preceding questions. Note that for the most part of this book, we will be using the AdventureWorks and AdventureWorksDW databases for our data mining activities as they already have the schema and dimensions predefined. We can use them to discuss how to extract the information rather than spending our time on defining the schema.

The details on how to acquire the AdventureWorks database is already discussed in the Preface of this book.

Consider an instance where you are a salesman for the AdventureWorks Cycles, company, and you need to make predictions that could be used in marketing the products. The problem sounds simple and straightforward, but any serious data miner would immediately come up with many questions. Why? The answer lies in the exactness of the information being searched for. Let's discuss this in detail.

The problem statement comprises the words predictions and marketing. When we talk about predictions, there are several insights that we seek, namely:

From the marketing point of view, several follow-up questions that must be answered are as follows:

We might observe that there are many questions that overlap the two categories and, therefore, there is an opportunity to consolidate the questions and classify them as follows:

Let's throw some light on these aspects based on the AdventureWorks example. We will get answers to the preceding questions and arrive at a more refined problem statement.

What is the population that we are targeting? The target population might be classified according to the following aspects:

What are the factors that we are actually looking at? They might be classified as follows:

What is the time period of the past data that we would be looking at? Usually, the data that we get is quite huge and often consists of the information that we might very adequately label as noise. In order to sieve effective information, we will have to determine exactly how much into the past we should look at; for example, we can look at the data for the past year, past two years, or past five years.

We also need to decide the future data that we will consider the data mining results for. We might be looking at predicting our market strategy for an upcoming festive season or throughout the year. We need to be aware that market trends change and so do people's needs and requirements. So we need to keep a time frame to refresh our findings to an optimal; for example, the predictions from the past five years' data can be valid for the upcoming two or three years depending upon the results that we get.

Now that we have taken a closer look into the problem, let's redefine the problem more accurately. AdventureWorks Cycles has several stores in various locations and, based on the location, we would like to get an insight into the following:

It is not necessary that we receive answers to all the detailed questions but even if we keep looking for the answers to these questions, there would be several insights that we will get, which will help us make better business decisions.