This initial phase focuses on understanding the project objectives and requirements from a business perspective, then converting this knowledge into a data mining problem definition and a preliminary plan designed to achieve the objectives.

The first objective of the data analyst is to thoroughly understand, from a business perspective, what the customer really wants to accomplish. Often the customer has many competing objectives and constraints that must be properly balanced. The analyst’s goal is to uncover important factors, at the beginning, that can influence the outcome of the project. A possible consequence of neglecting this step is to expend a great deal of effort producing the right answers to the wrong questions.

Background

Record the information that is known about the organization’s business situation at the beginning of the project.

Business Objectives

Describe the customer’s primary objective, from a business perspective. In addition to the primary business objective, there are typically other related business questions that the customer would like to address. For example, the primary business goal might be to keep current customers by predicting when they are prone to move to a competitor. Examples of related business questions are “How does the primary channel used (e.g., ATM, branch visit, Internet) affect whether customers stay or go?” or “Will lower ATM fees significantly reduce the number of high-value customers who leave?”

Business Success Criteria

Describe the criteria for a successful or useful outcome to the project from the business point of view. This might be quite specific and able to be measured objectively, for example, reduction of customer churn to a certain level, or it might be general and subjective, such as “give useful insights into the relationships.” In the latter case, it should be indicated who makes the subjective judgment.

Assess Situation

This task involves more detailed fact-finding about all of the resources, constraints, assumptions, and other factors that should be considered in determining the data analysis goal and project plan. In the previous task, your objective is to quickly get to the crux of the situation. Here, you want to expand upon the details.

• Inventory of Resources
• Requirements, assumptions, and constraints
• Risks and contingencies
• Terminology
• Costs and benefits

Inventory of resources

List the resources available to the project, including personnel (business experts, data experts, technical support, data mining experts), data (fixed extracts, access to live, warehoused, or operational data), computing resources (hardware platforms), and software (data mining tools, other relevant software).

Requirements, assumptions, and constraints

List all requirements of the project, including schedule of completion, comprehensibility and quality of results, and security, as well as legal issues. As part of this output, make sure that you are allowed to use the data. List the assumptions made by the project. These may be assumptions about the data that can be verified during data mining, but may also include non-verifiable assumptions about the business related to the project. It is particularly important to list the latter if it will affect the validity of the results. List the constraints on the project. These may be constraints on the availability of resources, but may also include technological constraints such as the size of dataset that it is practical to use for modeling.

Risks and contingencies

List the risks or events that might delay the project or cause it to fail. List the corresponding contingency plans, what action will be taken if these risks or events take place.

Terminology

Compile a glossary of terminology relevant to the project. This may include two components:

1. A glossary of relevant business terminology, which forms part of the business understanding available to the project. Constructing this glossary is a useful “knowledge elicitation” and education exercise.
2. A glossary of data mining terminology, illustrated with examples relevant to the business problem in question

Costs and benefits

Construct a cost-benefit analysis for the project, which compares the costs of the project with the potential benefits to the business if it is successful. The comparison should be as specific as possible. For example, use monetary measures in a commercial situation.

Determine data mining goals

A business goal states objectives in business terminology. A data mining goal states project objectives in technical terms. For example, the business goal might be “Increase catalog sales to existing customers.” A data mining goal might be “Predict how many widgets a customer will buy, given their purchases over the past three years, demographic information (age, salary, city, etc.), and the price of the item.”

• Data mining goals
• Data mining success criteria

Data mining goals

Describe the intended outputs of the project that enable the achievement of the business objectives.

Data mining success criteria

Define the criteria for a successful outcome to the project in technical terms—for example, a certain level of predictive accuracy or a propensity-to-purchase profile with a given degree of “lift.” As with business success criteria, it may be necessary to describe these in subjective terms, in which case the person or persons making the subjective judgment should be identified.