BISY3001 Business Analysis Report Based on Data Mining Concepts

Assessment Description   The goal of this assessment is to develop the business analysis skills of the students 

Task: Determine business objectives 

The first objective of the analyst is to thoroughly understand, from a business perspective, what the client 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 of the project that can influence the final outcome. A likely consequence of neglecting this step would be to expend a great deal of effort producing the correct answers to the wrong questions. 

1.1 Identify the Problem Area (100 words) 

Identify the problem area (e.g., Marketing, Customer Care, Business Development, etc.). Describe the problem in general terms. Check the current status of the project (e.g., Check if it is already clear within the business unit that we are performing a data mining project or do we need to advertise data mining as a key technology in the business?).   Clarify prerequisites of the project (e.g., what is the motivation of the project? Does the business already use data mining?). Identify target groups for the project result (e.g., Do we expect a written report for top management, or do we expect a running system that is used by naive end users?).   Identify the users’ needs and expectations.

1.2 Output: Business objectives (100 words) 

Describe the customer’s primary objective, from a business perspective, in the data mining project. In addition to the primary business objective, there are typically a large number of 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, while secondary business objectives might be to determine whether lower fees affect only one particular segment of customers. Informally describe the problem which is supposed to be solved with data mining. Specify all business questions as precisely as possible.  Specify any other business requirements (e.g., the business does not want to lose any customers).   Specify expected benefits in business terms. 

1.3 Output: Business success criteria (100 words) 

Describe the criteria for a successful or useful outcome to the project from the business point of view. This might be quite specific and readily measurable, such as reduction of customer churn to a certain level or general and subjective such as “give useful insights into the relationships.” In the latter case it should be indicated who would make the subjective judgment. Specify business success criteria (e.g., enrolment rate increased by 20 percent). Identify who assesses the success criteria. Each of the success criteria should relate to at least one of the specified business objectives. 

2. Assess the situation (200 words)

2.1 Activities: Inventory of resources (100 words) 

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

2.2 Activities: Sources of data and knowledge (100 words) 

Identify data sources.  Identify type of data sources (on-line sources, experts, written documentation, etc.).  Identify knowledge sources.  Identify type of knowledge sources (online sources, experts, written documentation, etc.).   Check available tools and techniques. Describe the relevant background knowledge (informally or formally). 

3. Requirements, assumptions and constraints (250 words) 

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, which can be checked during data mining, but may also include non-checkable assumptions about the business upon which the project rests. It is particularly important to list the latter if they form conditions on the validity of the results. List the constraints made on the project. These constraints might involve lack of resources to carry out some of the tasks in the project within the timescale required or there may be legal or ethical constraints on the use of the data or the solution needed to carry out the data mining task. 

List the risks, that is, events that might occur, impacting schedule, cost or result. List the corresponding contingency plans; what action will be taken to avoid or minimize the impact or recover from the occurrence of the foreseen risks. Identify business risks (e.g., competitor comes up with better results first). Identify organisational risks (e.g., department requesting project not having funding for project). Identify financial risks (e.g., further funding depends on initial data mining results). Identify technical risks. Identify other risks that depend on data and data sources (e.g. poor quality and coverage). Determine conditions under which each risk may occur. Develop contingency plans.

4. Determine data mining goals (250 words)

4.1 Determine data mining goals 

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

Describe the intended outputs of the project that enable the achievement of the business objectives. Note that these are normally technical outputs. 

Activities: Translate the business questions to data mining goals (e.g., a marketing campaign requires segmentation of customers in order to decide whom to approach in this campaign; the level/size of the segments should be specified). Specify data mining problem type (e.g., classification, description, prediction and clustering).