Microsoft Word - T2 2020 BISY3001 A3 Briefing.docx Unit Assessment Type Assessment Number Assessment Name Weighting Alignment with Unit and Course Due Date and Time Group Assignment A3 Business case...

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Microsoft Word - T2 2020 BISY3001 A3 Briefing.docx
Assessment Type
Assessment Number
Assessment Name
with Unit
and Course
Due Date and Time
Group Assignment
Business case understanding (Business analysis report based on data mining
Assessment Description The goal of this assessment is to develop the business analysis skills of the students
through a real-world scenario. In order to do that, each group consists of maximum three
students will choose a public data set from the following links after consulting with the
Links to public data set:
• KDnuggets
• Kaggle
• UC Irvine Machine Learning Repository
After choosing a data set, let’s assume, you have been hired as a data miner / business
analyst to do a thorough data mining process on the initiative for an organisation. To do the
work properly you will need to consider (and do as you see you fit) all the activities
described in the attached document on the data mining process (make any assumptions if
Detailed Submission
Business case understanding (1,000 words)
1. Determine business objectives (300 words)
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
Week 7, Friday, 07 May 2021, 11:59 pm via Moodle.

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).

Task Marks
1. Determine business objectives (300 words) 2
2. Assess the situation (200 words) 2
3. Requirements, assumptions and constraints (250 words) 6
4. Determine data mining goals (250 words) 5

Misconduct • Engaging someone else to write any part of your assessment for you is classified
as misconduct.
• To avoid being charged with Misconduct, students need to submit their own work.
• Remember that this is a Turnitin assignment and plagiarism will be subject to severe
• The AIH misconduct policy and procedure can be read on the AIH website
Late Submission • Late submission is not permitted, practical submission link will close after 1 hour.
Special consideration • Students whose ability to submit or attend an assessment item is affected by
sickness, misadventure or other circumstances beyond their control, may be
eligible for special consideration. No consideration is given when the condition or
event is unrelated to the student's performance in a component of the assessment,
or when it is considered not to be serious.
• Students applying for special consideration must submit the form within 3 days of
the due date of the assessment item or exam.
• The form can be obtained from the AIH website (
students/student-forms/) or on-campus at Reception.
• The request form must be submitted to Student Services. Supporting evidence
should be attached. For further information please refer to the Student
Assessment Policy and associated Procedure available on

Rubrics Marking criteria
ULO1: Demonstrate
broad understanding
data mining and
business intelligence
and their benefits to
business practice.

ULO3: Analyse
appropriate models and
methods for
classification, prediction,
reduction, exploration,
affinity analysis, and
customer segmentation
to data mining

ULO4: Propose a data
mining approach using
real business cases as
part of a business
intelligence strategy
addresses all
the tasks.

consists of

(13-15 marks)
addresses all
the tasks.

Report consists
of a few number
of mistakes.

(10-12 marks)
addresses most
of the tasks.

Report consists
of a few number
of mistakes.

(7-9 marks)
addresses a few
of the tasks.

Report consists
of a good
number of

(5-6 marks)

Unable to
tion/ DM
activities/ DM

(0-4 marks)
Answered 3 days AfterMay 03, 2021BISY3001


Neha answered on May 07 2021
28 Votes

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