business intillegence

Description Marks out of Wtg(%) Word

Count

Due

date

Assignment 4 Written and Practical Report 100 (55%) 4500 30/05/14

Assignment 4 relates to the specific course learning objectives 1, 2 and 4 and associated MBA

program learning goals and skills: Global Content, Problem solving, Change, Critical thinking,

and Written Communication at level 3.

1. demonstrate applied knowledge of people, markets, finances, technology and management in

a global context of business intelligence practice (data warehouse design, data mining process,

data visualisation and performance management) and resulting organisational change and how

these apply to implementation of business intelligence in organisation systems and business

processes

2. identify and solve complex organisational problems creatively and practically through the

use of business intelligence and critically reflect on how evidence based decision making and

sustainable business performance management can effectively address real world problems

4. demonstrate the ability to communicate effectively in a clear and concise manner in written

report style for senior management with correct and appropriate acknowledgment of main ideas

presented and discussed.

The key frameworks, concepts and activities covered in modules 2–12 and more specifically modules 6 to 12 are particularly relevant for this assignment. This assignment consists of three

tasks 1, 2 and 3 and builds on the research and analysis you conducted in Assignment 2. Task 1

is concerned with developing and evaluating a model of key factors impacting on credit risk

ratings for loan applications in determining whether approve a loan or not approve a loan. Task

2 is concerned with the key opportunities and challenges associated with the implementation

and utilisation of business intelligence systems. Task 3 is concerned with performance

management and provides you with the opportunity to design and build an interactive sales

performance dashboard with drill down capability using Tableau 8.0 Desktop or pivot tables.

Task 1 (Worth 40 marks)

In Task 1 of this Assignment 4 you are required to follow the six step CRISP DM process and

make use of the data mining tool RapidMiner to analyse and report on the creditrisk_train. csv

and creditrisk_score.csv data sets provided for Assignment 4. You should refer to the data

dictionary for creditrisk_train.csv (see Table 1 below). In Task 1 and 2 of Assignment 4 you

are required to consider all of the business understanding, data understanding, data preparation,

modelling, evaluation and deployment phases of the CRISP DM process.

Table 1 Data Dictionary for

creditrisk_train.csv Variable

Description

Row.No Unique identifier for each row – integer

Application.ID Unique identifier for loan application – integer

Credit.Score Credit score given to the loan application

This is a measure of the creditworthiness of the applicant. (http://en.wikipedia.org/wiki/Credit_score _in_the_United_States) http://www.buzzle.com/articles/credit-score-

rating-scale.html

Late.Payments History of late payments with existing loans

Months.In.Job Months in current job

Debt.To.Income.Ratio The Percentage Of consumer’s gross income that goes toward paying debts (http://en.wikipedia.org/wiki/Debt_to_inc ome_ratio)

Loan.Amount Loan amount requested

Liquid.Assets Liquid assets

Num.Credit.Lines Number of credit lines

Credit.Risk Credit risk rating (Very Low, Low, Moderate,

High, Do not lend)

http://www.dico.com/design/Publications/En/

By-law5-CommercialLendingPractices-

May2005-

UpdatedMay2008/CreditRiskRatings.pdf a) Research the concepts of credit risk and credit scoring in determining whether a financial

institution should lend at an appropriate level of risk or not lend to a loan application. This will

provide you with a business understanding of the dataset you will be analysing in Assignment 4.

Identify which (variables) attributes can be omitted from your credit risk data mining model and why. Comment on your findings in relation to determining the credit risk of loan applicants.

b) Conduct an exploratory analysis of the creditrisk_train.csv data set. Are there any missing

values, variables with unusual patterns? How consistent are the characteristics of the

creditrisk_train.csv and creditrisk_score.csv datasets? Are there any interesting relationships

between the potential predictor variables and your target variable credit risk? (Hint: identify the

variables that will allow you to split the data set into subgroups). Comment on what variables in the

data set creditrisk_train.csv might influence differences in credit scores and credit risk ratings and

possible approval or rejection of loan applications?

c) Run a decision tree analysis using RapidMiner. Consider what variables you will want to include

in this analysis and report on the results. (Hint: Identify what is your target variable and what are

your predictor variables?) Comment on the results of your final model.

d) Run a neural network analysis using RapidMiner, Again consider what variables you will want

to include in this analysis and report on the results. (Hint: Identify what is your target variable and

what are your predictor variables?) Comment on the results of your final model.

e) Based on the results of the Decision Tree analysis and Neural Network analysis - What are the

key variables and rules for predicting either good credit risk or bad credit risk? (Hint: with

RapidMiner you will need to validate your models on the creditrisk_train.csv data using a number

of validation processes for the two models you have generated previously using decision trees and

neural network models). Comment on your two predictive models for credit risk scoring in relation

to a false/positive matrix, lift chart and ROC chart (Note: for the evaluation operator reports – a Lift

chart and a ROC chart you will need to convert the target variable credit.risk to a nominal variable

with two values (Good and Bad). Comment on the results of your final model.

Overall for Task 1 you need to report the output of each analysis in sub task activities a to e

and briefly comment on the important aspects of each analysis and relevance to customer

segmentation, behaviours and propensity to default on a loan (Note you will find the North

Text book an invaluable reference for the data mining process activities (Approx 1500 words).

Note the important outputs from your statistical analyses in RapidMiner should be included as

appendices in your report to provide support your conclusions regarding each analysis and are

not included in the word count

Task 2 (Worth 15 marks)

For the deployment phase of the CRISP DM process discuss the key opportunities and

challenges including socio-technical change management associated with the implementation

and utilisation of a business intelligence system which supports improved evidence based

decision making in organisations. (1500 words approx.)

Task 3 (Worth 35 marks) Scenario Global Bike International (GBI) is a world class bicycle company serving both professional and amateur cyclists. The company sells bicycles and accessories. In the touring bike

category, GBI’s handcrafted bicycles have won numerous design awards and are sold in over

10 countries. GBI’s signature composite frames are world-renowned for their strength, low

weight and easy maintenance. GBI bikes are consistently ridden in the Tour de France and

other major international road races.

GBI produces two models of their signature road bikes, a deluxe and professional model. The

key difference between the two models is the type of wheels used, aluminium for the basic

model and carbon composite for the professional model. GBI’s off-road bikes are also

recognized as incredibly tough and easy to maintain. GBI off-road bikes are the preferred

choice of world champion off road racers and have become synonymous with performance

and strength in one of the most gruelling sports in the world.

GBI produces two types of off-road bike, a men’s and women’s model. The basic difference

between the two models is the smaller size and ergonomic shaping of the women’s frame.

GBI also sells an accessories product line comprised of helmets, t-shirts and other riding

accessories.

GBI partners with only the highest quality suppliers of accessories which will help enhance

riders’ performance and comfort while riding GBI bikes. The Figure below displays the GBI

range of products.

Traditionally GBI was a wholesaler who sold their bikes to retailers who then resold the bikes

to the end consumers. Recently GBI has decided to sell their bike to the end consumer via the

internet.

Organisational Structure

Rules have been kept simple: GBI’s headquarters are located in Dallas and the European subsidiary company (GBI Europe) is based in Heidelberg, Germany. In regards to the GBI sales process there are two sales

organisations for America (Eastern US and Western US) and two for Germany (Northern

Germany and Southern Germany). All sales organisations have a wholesale distribution

channel responsible for delivering the products to the customers. However only one sales

organisation is required in each country to support internet sales. The diagram below displays

the GBI organisation to support the sales process.

Dashboard GBI management require a sales dashboard to be created to provide greater insight to their sales data to understand the trends and sales performance. They want the flexibility to

visualize sales data in a number of different ways. They want to be able to get a quick

overview of the data and then be able to zoom and filter on particular aspects and then get

further details as required.

The specific information they are concerned with is the following four sales performance

reports.

1. Sales Revenue and Sales Gross Profit by Week, Month, and Year

2. Sales Revenue and Sales Gross Profit by Product/Product Category

3. Sales Revenue and Sales Gross Profit by sales organisation

4. Sales Revenue and Sales Gross Profit by country

The CEO of the GBI needs each morning an overview of how the company is performing. He

has a very busy schedule and needs the information to be displayed in less than 5 seconds.

The data has been extracted from the GBI’s SAP enterprise resource planning system and has

been made available in spread sheet format.

You can re-organise the spread sheet as you determine necessary to support the dashboard.

GBI Sales spread sheet data set is available on the course study desk

Your task 3 is create

(a) dashboard to satisfy the GBI management requirements for the four specified sales

performance reports:

1. Sales Revenue and Sales Gross Profit by Week, Month, and Year

2. Sales Revenue and Sales Gross Profit by Product/Product Category

3. Sales Revenue and Sales Gross Profit by sales organisation

4. Sales Revenue and Sales Gross Profit by country

(b) provide a rationale for the graphic design and functionality that is provided in your

dashboard for GBI in terms of how it meets GBI management requirements for four specified

sales performance report (1000 words approx). You will need to submit your Tableau

workbook in .twbx format which contains your dashboard as a separate document to your main

report for Assignment 4.

Report presentation, and quality of argument appropriately supported by relevant number of references (worth 10 marks) The assignment 4 report must be structured as follows:

1. Cover page for assignment 4 report

2. Executive summary

3. Table of contents

4. Body of report – main sections and subsections for each Task and sub task such as Task 1 sub task a) etc…

5. List of References

6. Appendices to accompany Task 1 data mining analyses

Harvard referencing resources Install a reference tool (example Endnote) which integrates with your word processor. These

tools are a great help for referencing and citing sources in your assignments. For more

information on how to get Endnote you may visit the following webpage:

<http://www.usq.edu.au/library/infoabout/endnote/default.htm>.

Study the referencing techniques in Communication skills handbook (Smith & Summers

2010).

USQ Faculty of Business Librarian Adrian Stagg has compiled the following resources on

how to reference correctly using the Harvard referencing system – make use of these excellent

resources if you are unsure as how to reference correctly using Harvard referencing system.

Library Guides <http://www.usq.edu.au/library/help/ehelp/ref_guides/default.htm> Guide to Harvard (Breeze)

<http://www.usq.edu.au/library/Breeze/Fac_Business/HarvardAGPS> PDF Brief Guide

<http://www.usq.edu.au/library/Breeze/Fac_Business/Harvard_AGPS/Harvard_AGPS_PDF_

Guide.pdf> Warnings

 This assignment must be the expression of your own work. It is acceptable to discuss

course content with others to improve your understanding and clarify requirements,

but solutions to assignment questions must be done on your own. This also means that

it is not sufficient to merely paraphrase the entire assignment content from a textbook

or other sources. Your assignment answers need be a reflection and synthesis of your

research of the associated topics.

 You need to demonstrate your understanding of associated topics for each assignment.

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. This may lead to severe

academic penalties as outlined in Academic Regulation 5.10 of the USQ Handbook. It

is your own responsibility to ensure the integrity of your work. Refer to the Faculty of

Business guidelines for further details.

 An indiscriminate overuse of incorrectly referenced or cited web pages in your

assignment will result in poor marks.

Assignment 4 submission details All assignments must be submitted electronically via the Ease assignment submission

system and are subject to checking for plagiarism and collusion via Turnitin. You will

be provided with a link to Turnitin on the course study so you can submit your

assignment to Turnitin to obtain an originality report. You must also include an

originality report on your assignment from Turnitin which provides a check on the

integrity and originality of your assignment work.

Note carefully University and Faculty policy on plagiarism, collusion and cheating. If

any of these occur they will be found and dealt with.

Grading scheme Your assignment will be assessed on content and style. Content refers to the way in which

your assignment reflects breadth and depth of understanding of the topics and knowledge of

business intelligence systems as covered in the course so far and relevant other researched

literature. Style relates to the adherence to requirements including word counts, use of

English, spell checking, proof reading as well as report presentation.

You are required to use and cite a suitable number of references of high quality. For example, it

is not sufficient for most references to be Internet websites as these can be of questionable

quality. Academic conventions require that you acknowledge any use of ideas from others. In

most cases this means stating which book or article is the source of the idea or quotation. You

are required to follow the Harvard referencing system for both in-text citations and list of

references and to clearly distinguish between your own ideas, other’s ideas adapted to your own, and ideas taken directly from the literature. You must support your views with

appropriate and relevant literature citations.

The report should be an insightful discussion and not just a descriptive treatment of the topic.

A major emphasis for the assignment will be on a structured report that clearly outlines the

topic and/or issues to be discussed. It will include a cover page, executive summary, table of

contents, a report body that uses headings and paragraphs to clearly detail descriptions,

explanations or arguments, list of references and list of appendices. Your report should

specifically focus on the key issues of reducing mobile phone customer churn and improving

mobile phone customer lifetime value in relation to Business intelligence and specific data

mining of a customer churn data set, a balanced scorecard and strategy map to implement

and evaluate the above proposed strategy and discussion of design and implementation

issues associated with a data warehouse in the context of a mobile phone network service

provider. Your report should not be just a simple description of the topics involved.

If you are not familiar with the requirements of a formal report format, refer to the

Communication skills handbook. You may also find the OPACS Academic Learning Support

web-site useful: <http://www.usq.edu.au/opacs/ALSonline>.