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August 12th, 2017 
admin BUSINESS INTELLIGENCE
Description
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 for predicting for whether bank customers are likely to respond positively a marketing campaign for a new product – a new term deposit with a bank.
Task 2 is concerned with the key opportunities and challenges associated with the utilisation of big data analytics and in particular sentiment analysis.
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 8.3 Desktop software.
Task 1 (Worth 40 marks)
The goal of task 1 is to predict the likelihood of a bank customer responding positively to the marketing campaign for a bank product – a bank term deposit. The bank data set for Assignment 4 Task 1 is derived from the direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls to bank customers. Often, more than one contact to the same client was required, in order to assess if the new bank product (bank term deposit) would be (or not) subscribed. Hence the question we trying to answer: Is Customer is likely to respond positively to marketing campaign for a bank product – a bank term deposit.
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 bank_train. csv and bank_test.csv data sets provided for Assignment 4. You should refer to the data dictionary for bank_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.
Data dictionary for bank data set variables
Variable name
Type of variable and possible values
1 – age
Age of the customer (numeric)
2 – job :
type of job (categorical) : “admin”, “unknown”, “unemployed”,”management”,”housemaid”,”entrepreneur”,”student”, “blue-collar”,”self-employed”, “retired” ,”technician”, “services”)
3 – marital :
marital status (categorical): (“married”, “divorced”, “single”; note: “divorced” means divorced or widowed)
4 – education
(categorical): (“unknown”,”secondary”,”primary”, “tertiary”)
5 – default:
has credit in default? (binary): (“yes”,”no”)
6 – balance:
average yearly balance, in euros (numeric)
7 – housing:
has housing loan? (binary): (“yes”,”no”)
8 – loan:
has personal loan? (binary): “yes”,”no”) # related with the last contact of the current campaign:
9 – contact:
contact communication type (categorical): “unknown”, “telephone”, “cellular”)
10 – day:
last contact day of the month (numeric)
11 – month:
last contact month of year (categorical): “jan”, “feb”, “mar”, …, “nov”, “dec”)
12 – duration:
last contact duration, in seconds (numeric) # other attributes:
13 – campaign:
number of contacts performed during this campaign and for this client (numeric), includes last contact
14 – pdays:
number of days that passed by after the client was last contacted from a previous campaign (numeric) , -1 means client was not previously contacted)
15 – previous:
number of contacts performed before this campaign and for this client (numeric)
16 – poutcome:
outcome of the previous marketing campaign (categorical): “unknown”, “other”,”failure”,”success”)
Output variable
(desired target):
17 – y –
has the client subscribed to a term deposit? (binary): “yes”,”no”
Missing Attribute Values: None
a) Research consumer purchase behaviour to determine key factors influencing the decision by a customer to open a new bank account such as a term deposit. This will provide you with a business understanding of the dataset you will be analysing in Assignment 4. Identify which (variables) variables can be omitted from your bank term deposit data mining model and why. Comment on your findings in relation to determining the factors likely to influence a customer opening a bank account – term deposit.
b) Conduct an exploratory analysis of the bank_train.csv data set. Are there any missing values, variables with unusual patterns? How consistent are the characteristics of the bank_train.csv with bank_test.csv dataset? Are there any interesting relationships between the potential predictor variables and your target variable Outcome is a customer likely to subscribe to a term deposit? (Hint: identify the variables that will allow you to split the data set into subgroups). Comment on what variables in the data set bank_train.csv might influence differences in the Outcome of a marketing campaign where bank customers are contacted by phone and asked if they wish to subscribe to a bank term deposit?
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 logistic regression 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 Logistic Regression analysis – What are the key variables and rules for predicting whether a marketing campaign inviting customers to open a new bank account – a term deposit will have positive (yes) or negative (no) outcome? (Hint: with RapidMiner you will need to validate your models on the bank_train.csv data using a number of validation processes for the two models you have generated previously using decision trees and logistic regression analysis models). Comment on your two predictive models for predicting the likelihood of a customer opening a new bank account – a term deposit 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 outcome to a nominal variable with two values (Yes and No). 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 bank customer behaviours and propensity to open a new bank account – a term deposit (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 Assignment 3 report to provide support your conclusions reached regarding each analysis and are not included in the word count
Task 2 (Worth 20 marks)
a) What might be some of the long term societal effects of datafication? Datafication is the consequence of the following. Network connected digital devices such as laptops, tablets and smart phones are increasingly in continuous use by individuals and thus are capable of monitoring of every minute of an individual’s everyday life”. Such data are often processed by pre-determined algorithms that lead to decisions that follow on directly without further human intervention (often with the claim that the decisions are for the individual’s benefit). Discuss what are some of the key privacy ethical and security concerns for organisations and individuals with business and government increasingly adopting a big data analytics and algorithmic approach to decision making. To what extent are governments and legislation keeping pace with this phenomena (1500 words approx.)
Task 3 (Worth 30 marks)
Scenario Dashboard
World Bank Regional Unit for African continent are responsible for facilitating economic development and improved quality of life in Africa. They would like to have a technology adoption dashboard built for the African continent. They would like to have a better understanding of key drivers of technology adoption. In particular they would like to see how (1) number of landline connections, (2) mobile phone connections and internet connections per head of population and urbanization impacts on the rate of technology adoption over time across the African continent and for regions and specific countries in regions of the African continent.
The World Bank Regional Unit for African Continent want the flexibility to visualize how each of the key technology adoption drivers listed previously impact on the rate of technology adoption in African continent for regions and specific countries 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 technology adoption performance reports.
1. Top 10 best performing countries in terms of technology adoption of mobile phones by year and region
2. Top 10 worst performing countries in terms of technology adoption of Internet by year and region
3 Top 10 best performing countries in terms of technology adoption of landlines per head of population by year and region
4. A summary of key technology adoption factors for each region of Africa for a given year
The data has been extracted from the relevant World Bank data sources and has been made available in a spreadsheet format for this assignment 4.
Your task 3 is create
(a) A visual dashboard to satisfy the requirements of World Bank Regional Unit for African continent responsible for facilitating economic development and improvements in quality of life for the African continent based on the data set provided (AfricaTA-1995-2013.xlsx. Four specified technology adoption performance reports are required which can be viewed at the continent, region and nation levels visually and in terms of the numeric data:
1. Top 10 best performing countries in terms of technology adoption of mobile phones by year and region
2. Top 10 worst performing countries in terms of technology adoption of Internet by year and region
3 Top 10 best performing countries in terms of technology adoption of landlines per head of population by year and region
4. A summary of key technology adoption factors for each region of Africa for a given year
You should briefly discuss the key findings for each of these reports
(b) Provide a rationale for the graphic design and functionality that is provided in your dashboard for World Bank Regional Unit for African continent, regions and nations in terms of how it meets their requirements for four specified technology adoption performance reports (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… Task 2 task
Task 3 sub task 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/referencing/endnote-bibliographic-software .
Study the referencing techniques in Communication skills handbook (Smith & Summers
2010).USQ Librarian 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 Harvard Referencing Guide <http://www.usq.edu.au/library/referencing/harvard-agps-referencing-guide>
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.
? 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 course studyAssignment 4 submission link and are subject to checking for plagiarism and collusion by Turnitin when you submit your Assignment 4 via the Assignment 4 submission link.
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 practical application of Rapid Miner and Tableau, 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 as covered in the course so far and relevant other research 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 application and 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 application of data mining and business performance management and 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.
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