Business Intelligence Systems

UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 1 of 10
Business Intelligence Systems– UUIS8008
Assignment – 1
Semester 2 – 2018
You are expected to submit professionally presented word-processed assessment documents.
This includes:
A title page showing: ID number/s, name/s, lecturers’ name/s, and assessment title.
Correct spelling and appropriate use of grammar.
Pages numbered including a contents page.
Stapled or bound (no paper clips/plastic folders or plastic sleeves).
Questions correctly labelled and numbered with clear and consistent headings
Line spacing no less than 1.5 and no greater than double. Main text using 12pt font size.
A complete reference list should be included at the back of the assessment using Harvard AGPS style. of
referencing with in-text citation.

Assessment Marks Weighting Issue Date Due Date
Assignment 1 100 35% 30/07/2018 22/08/2017

 

Description : Written Report Writing and Practical Activity:
Learning
Objectives
:
Applicable course objective:
– LO-1: Demonstrate applied knowledge of people, markets, finances, technology and
management in a global context of business intelligence and understand the necessity of
data driven decision-making.
– LO-2: Understand the resulting organisational change for business intelligence practice
(data warehouse design, data mining process, data-visualisation and performance
management) and how these apply to ……business processes.
– LO-3: Identify and solve complex organisational problems creatively and practically
through ……problems.
– LO-5: Demonstrate the ability to communicate effectively in a clear and concise manner in
written report style for senior management.
Word Limit
Plagiarism : Maximum 15% similarity

: Maximum 3200; however +/-(10%) acceptable
Family Name: ____________
First Name:
______

Student ID: __________________
________________ _____

Postgraduate Certificate in Information Systems – Level8
UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 2 of 10
Submission Guidelines on uPortal
Firstly, upload your MS Word (.docx) file in the “Turnitin Check Link: Assignment-1”. Then download your
Turnitin originality PDF report from Turnitin. (It should be less than or equal 15% similarity to accept the
submission)
* You can see your Turnitin originality report (PDF) easily which is displayed under “File Submission”. Click
on the percentage % to see your detailed Turnitin originality report.
Secondly, attach the your downloaded Turnitin (.pdf) report file and Tableau Packaged Workbook
(.twbx)
files (using any version from 2018.1 to current) using the naming convention below, to your
online assignment submission link in the Assignment-1 area on the UUIS8008 study desk before or on
the day the assignment is due.
1.
[Student id] _ [Student First Name] _ [course code] _ Asg1.docx
(eg. 700XXX_John_UUIS8008_Asg1.docx)
2.
[Student id] _ [Student First Name] _ [course code] _ Asg1.twbx
(eg. 700XXX_John_UUIS8008_Asg1.twbx)
Note: If any other format used resulting assignment files cannot be opened by the marker, it may be
treated as late until a suitable replacement is received. For example-
.docx and .twb file will not be
accepted. Maximum allowable file size is 20 MB/ per file.
Assignment 1 consists of three main tasks and a number of sub tasks
Important Instructions
This assignment must be the expression of your own work. It is acceptable to discuss general
course content with others to improve your understanding and clarify requirements, but
solutions to this assignment question must be done on your own. You must not copy from
anyone, including tutors and fellow students, nor provide copies of your work to others.
Assignments that do not adhere to this requirement will be deemed as being the result of
collusion or plagiarism.

UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 3 of 10
Task 1 Data Driven Decision Making (10+5+10=Worth 25 Marks)
Data driven decision-making – (3D) – Increasingly organisations are looking to make decisions
that are based on the evidence of real data. Data analytics toolsets are evolving rapidly and
many organisations have invested heavily in information architecture so that they have the
capability to move transactional data and external data into data warehouses and provide endusers with specific data analytics software applications to support evidence based (data driven)
decision making. However, many organisations are still struggling to make the transition to a
data driven decision making paradigm.
Your role is the Lead Data Analyst in CoreLogic NZ Limited, who has office in both
Wellington and Auckland city. The head office address is located at Level 2, 275 Cuba St, PO
Box 4072, Wellington 6140, New Zealand. CoreLogic is a leading property information,
analytics and services provider in the New Zealand. Now you are required to conduct a relevant
literature review regarding data driven decision-making (3D) and prepare a strategic briefing
report of about 1200 words for the Frank Martell, Chief Executive Officer, of
CoreLogic NZ Limited on key aspects of data driven decision making as outlined below:
Task 1.1) Identify from the existing literature and discuss the relevant decision making
theories and frameworks which would inform a deep or through understanding of the
decision making process in organisations (about 600 words).
Task 1.2) Provide a comprehensive definition of data driven decision making and
explain briefly how your definition has been informed by specific literature on data
driven decision-making (about 100 words).
Task 1.3) Based on an understanding of how decisions are made in organisations,
critically analyze and evaluate how changes in organisational decision making process
would be important for the CoreLogic NZ Limited for its successful transition to a data
driven decision (3D) making paradigm (about 600 words).

UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 4 of 10
Task 2 Exploratory Data Analysis, Decision Tree and Linear Regression model Analysis
(15+10+10=Worth 35 Marks)
Now-a-days Kiwi commercial banks are using data mining techniques, machine learning,
statistics and artificial intelligence for analysing the credit worthiness of a borrower. Assume you
are a credit analyst of ASB bank and now you are required to do the following tasks 2.1 to 2.3
before approving credit decision to each new loan applicant by using ASB bank’s
creditdata.csv
data set provided in uPortal UUIS8008 Assignment-1 URL link and Data Dictionary in
Appendix-A.
Task 2.1) Using RapidMiner Studio data mining tool conduct an in-depth exploratory data
analysis of the
creditdata.csv data set. Summarise the key findings of your exploratory data
analysis –
in terms of describing key characteristics of each of the variables in the creditdata.csv
data set such as maximum, minimum values, average, standard deviation, most frequent values
(mode), missing values, inconsistency and relationships with other variables if relevant, – in a
table named
Table 2.1: Results of Exploratory Data Analysis for the creditdata.csv Data Set.
Also, Identify top five (5) key variables, which contribute to determining whether a potential
loan applicant is a good credit risk or a bad credit risk and the rationale for why you have
selected your five top variables for predicting credit risk. Discuss each of your five top variables
in terms of the results of your exploratory data analysis (About 700 words).
Task 2.2) Build a Decision Tree model for predicting Credit Risk based on the creditdata.csv
data set using RapidMiner and an appropriate set of data mining operators and a reduced set of
variables from
creditdata.csv determined by your exploratory data analysis in Task 2.2. Provide
the following for Task 2.2:
(i) (1) Final Decision Tree Model process, (2) Final Decision Tree diagram, and (3)
Decision tree rules.
(ii) Briefly explain your final Decision Tree Model Process, and discuss the results of the
Final Decision Tree Model drawing on the key outputs (Decision Tree Diagram,
Decision Tree Rules, model performance) for predicting credit risk. This discussion
should be based on the contribution of each of the top five variables to the Final Decision
Tree Model and relevant supporting literature on the interpretation of decision trees
(About 300 words).

UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 5 of 10
Task 2.3) Build a Linear Regression model for predicting the creditworthiness of an
applicant using a RapidMiner data mining process and an appropriate set of data mining
operators and a reduced set of variables from the
creditdata.csv data set determined by your
exploratory data analysis in Provide the following for Task 2.3:
(i) A screen capture of Final Linear Regression Model process and briefly describe your
Final Linear Regression Model process
(ii) A table named Table 2.4 named Results of Final Linear Regression Model for Task
2.4 for
creditdata.csv data set.
(iii) Discuss the results of the Final Linear Regression Model for
creditdata.csv data set
drawing on the key outputs (coefficients, standardised coefficients, t-statistics values, pvalues and significance levels etc.) for predicting house prices and relevant supporting
literature on the interpretation of a Linear Regression Model (About 300 words).
Include all appropriate RapidMiner outputs such as RapidMiner Processes, Graphs and Tables
that support the key aspects of your exploratory data analysis, decision tree and linear regression
model analysis of the
creditdata.csv data set in your Assignment 1 report.
UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 6 of 10
Task 3 Sales Reports using Tableau Desktop (6+6+7+6=Worth 25 Marks)
The aim of every retail superstore business is attracting new customers, retaining existing
customers, and selling more to each customer. To ensure this, a retail superstore
business needs to offer customers the products they want at the right prices. Moreover, it
needs to ensure the right customer experience as well as profitability. In order to achieve the
aforementioned objectives, many retail businesses today are using data analytics and
visualization tools like Tableau Desktop. Assume you are a junior data analyst of New
Zealand based Business Intelligence (BI) consultant company of retail superstore located in
United States of America. You are an expert for Tableau Desktop analysis and now you are
required to create the sales report by covering the sub tasks 3.1 to 3.4.
With the given Excel file
SalesSuperstore_US.xlsx on the UUIS8008 course study desk
Assignment-1 link and by using Tableau Desktop produce the four following reports with
appropriate accompanying graphs based on a Tableau workbook sheet view for each. Briefly
comment on each report in about 150 words in terms of what trends and patterns are apparent
in each report.
The
SalesSuperstore_US.xlsx file contains the following dimensions and information with
10,000 data rows:
1. Customer Name, ID, Customer Segment
2. Location – Region, State, City, Post code
3. Product Category, Sub Category, Name, ID, Unit Price
4. Order Information
5. Shipping Information
6. Sales, Quantity, discount and Profit Information
Task 3.1) Create a report and accompanying table/graph using Tableau that shows a trend
analysis for sales by different product categories, sub categories for each quarter over the
years 2015 to 2018 and comment on key trends and patterns apparent in this report (About
150 words).
Task 3.2) Create a report and accompanying table/graph using Tableau that shows for each
Product Category Average Profit and Total Sales for each month over the years 2015 to 2018.
Find out the highest and lowest profitable category by specific time and sales volume in your
graphical view. Also comment on the other key trends and patterns apparent in this report
(About 150 words).
Task 3.3) Develop a geographical map presentation using Tableau that shows graphically the
relative size by city within each state, by product total sales and profit for year 2016 and
comment on key trends and patterns in this report (About 150 words).

UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 7 of 10
Task 3.4) Prepare a report and accompanying table/graph using Tableau that shows for
Product Sub Categories that are office supplies based average unit prices, total sales and
average profit for year-quarter-month (YY-QQ-MM) basis over the years from 2015 to 2018
and comment on key trends and patterns in this report (About 150 words).
Report presentation structure, Writing style and Referencing:
(5+5+5=Worth 15 Marks)
Your assignment-1 report must be structured in report format as follows:

Cover page for assignment 1 report
– Title Page
– Table of Contents
1. Body of report- main sections and subsections for assignment 1 task and sub tasks
1.1 Task 1.1 will be an appropriate sub headings etc….thn for each sub task 1.2 and
1.3 and so on……
2. Task 2.1, 2.2 ………..
3. Task 3.1, 3.2 ………
– List of References
– Appendices

Online Assignment submission
All assignments must be submitted electronically via the course study desk “Turnitin Check
Link: Assignment-1
” first. Then, Turnitin (plagiarism software) performs an automated
checking for plagiarism, collusion and cheating. After that, you need to submit Turnitin
generated originality report (.pdf) with Tableau file (.twbx) in the uPortal “
Assignment-1
submission link”
.
Note carefully UUNZ policy on Academic Misconduct such as plagiarism, collusion and
cheating. If any of these occur they will be found and dealt with by the UUNZ Academic
Integrity Procedures.
Harvard AGPS Referencing Requirement:
The Harvard AGPS referencing style and in-text citations must be used in appropriate places.
Study the referencing techniques for Harvard AGPS Referencing. UUNZ TPS (Tertiary
Programme Support) classes will help you to present your assignment in the correct report
writing format and Harvard AGPS style of referencing.

UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 8 of 10
Appendix A: Data Dictionary and Description of the creditdata.csv data set.
1. Title: ASB Bank Personal Credit dataset – creditdata.csv
2. Number of Instances: 1200
3. Number of Attributes: 22 (8 numerical, 14 categorical)
4. Table with Attribute description for creditdata.csv

Attribute Name Type of Attribute Range of attribute
1. Custno Customer Id Custno1 to Custno1200
2. Checking Status of existing checking account (qualitative) A: <= 0 NZD
B: <= 200 NZD
C: >= 200 NZD / Salary assignments for one year
D: No checking account
3. duration Duration in months of loan (numeric)
4. history Credit history (qualitative) A: no credits taken/ all credits paid back duly
B: all credits at this bank paid back duly
C: existing credits paid back duly till now
D: delay in paying off in the past
E: critical account/other credits existing (not at this bank)
5. purpose Purpose of proposed loan (qualitative) A: car
B: car (used)
C: furniture/equipment
D: radio/television
E: domestic appliances
F: repairs
G: education
H: (vacation)
I: retraining
J: business
K: others
6. amount Credit amount (numeric)

UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 9 of 10

7. savings Savings account/bonds (in New Zealand currency)
(qualitative)
A: < 100 NZD
B: 100 <= … < 500 NZD
C: 500 <= … < 1000 NZD
D: >= 1000 NZD
E: unknown/ no savings account
8. employed Present employment since (qualitative) A: unemployed
B: < 1 year
C: 1 <= … < 4 years
D: 4 <= … < 7 years
E: >= 7 years
9. instalp Instalment rate as percentage of disposal income
(numeric)
10. marital Personal status and sex (qualitative) A: male: divorced/separated
B: female : divorced/separated/married
C: male : single
D: male : married/widowed
E: female : single
11. coapp Other debtors / guarantors (qualitative) A: none
B: co-applicant
C: guarantor
12. resident Present residence since (numeric) in years
13. property Property (qualitative) A: real estate
B: if not A: building society savings agreement/life insurance
C: if not A/B: car or other, not in attribute 6
D: unknown / no property
14. age Age in years (numeric)
15. other Other instalment plans A: bank
B: stores
C: none

UUIS8008 Business Intelligence Systems Assignment – 1
UUNZ Institute Of Business: S2, 2018 Page 10 of 10

16. housing Housing (qualitative) A: rent
B: own
C : for free
17. excred Number of existing credits at this bank (numeric)
18. job Job (qualitative) A: unemployed/ unskilled – non-resident
B: unskilled – resident
C: skilled employee / official
D: management/ self-employed/
highly qualified employee/ officer
19. depends Number of dependent people; being liable to provide
maintenance for (numeric)
20. telephone Telephone A: none
B: yes, registered under the customer’s name
21. foreign Foreign worker (qualitative) A: yes
B: no
22. credit_rating Credit rating (qualitative) Good
Bad