CQUniversity Unit Profile
COIT20253 Business Intelligence using Big Data
Business Intelligence using Big Data
All details in this unit profile for COIT20253 have been officially approved by CQUniversity and represent a learning partnership between the University and you (our student).
The information will not be changed unless absolutely necessary and any change will be clearly indicated by an approved correction included in the profile.
General Information

Overview

Big data is a popular term used to describe the exponential growth and availability of structured and unstructured data. In this unit, you will explore big data within the context of business intelligence. In this unit, you will learn concepts of business intelligence, alignment of big data to business intelligence and how big data technologies can be used in building organisational business intelligence. You will learn how big data is changing businesses and how organisations can take advantage of big data in decision making. You will learn how organisations are integrating non-traditional unstructured data with the traditional structured enterprise data to do the business intelligence analysis. In order to understand these, you will learn big data analytical tools and technologies to help solve authentic business problems and make effective business decisions.

Details

Career Level: Postgraduate
Unit Level: Level 9
Credit Points: 6
Student Contribution Band: 8
Fraction of Full-Time Student Load: 0.125

Pre-requisites or Co-requisites

Prerequisites: COIT20250 e-Business Systems, COIT20245 Introduction to Programming and COIT20247 Database Design and Development. Anti-Requisites: If you have completed unit COIT20236 then you cannot take this unit.

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Offerings For Term 2 - 2020

Brisbane
Melbourne
Online
Sydney

Attendance Requirements

All on-campus students are expected to attend scheduled classes – in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Class and Assessment Overview

Recommended Student Time Commitment

Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Class Timetable

Bundaberg, Cairns, Emerald, Gladstone, Mackay, Rockhampton, Townsville
Adelaide, Brisbane, Melbourne, Perth, Sydney

Assessment Overview

1. Written Assessment
Weighting: 35%
2. Presentation
Weighting: 25%
3. Project (applied)
Weighting: 40%

Assessment Grading

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%). Consult the University’s Grades and Results Policy for more details of interim results and final grades.

Previous Student Feedback

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Feedback from Students during class

Feedback

Students were keen to learn new tools, especially tools for text mining and sentiment analysis, which are growing areas in business intelligence.

Recommendation

Identify and demonstrate open-source text mining and sentiment analysis tools during the tutorial sessions.

Feedback from 'Have your say' evaluation through Moodle Site.

Feedback

Multiple students experienced difficulties in understanding big data strategy and aligning it with the chosen datasets.

Recommendation

Provide more examples of big data strategy and demonstrate how to align it with the dataset.

Unit Learning Outcomes
On successful completion of this unit, you will be able to:
  1. Apply concepts and principles of big data to evaluate and explain how large volume of structured and unstructured data are managed in an organisation
  2. Analyse critically and reflect on how organisations are including non-traditional valuable data with the traditional enterprise data to do the business intelligence analysis
  3. Critically analyse and evaluate different big data technologies used for decision making in an organisation
  4. Develop big data strategy for data-centric organisations to meet client requirements
  5. Apply big data architecture, tools, and technologies for decision making and problem solving in the organisational context.

Australian Computer Society (ACS) recognises the Skills Framework for the Information Age (SFIA). SFIA is in use in over 100 countries and provides a widely used and consistent definition of ICT skills. SFIA is increasingly being used when developing job descriptions and role profiles.

ACS members can use the tool MySFIA to build a skills profile at https://www.acs.org.au/professionalrecognition/mysfia-b2c.html

This unit contributes to the following workplace skills as defined by SFIA. The SFIA code is included:

  • Research(RSCH)
  • Data Management (DATM)
  • Emerging Technology Monitoring (EMRG)
  • Data Analysis (DTAN)
  • Application Support (ASUP) 
  • Analytics (INAN)

Alignment of Learning Outcomes, Assessment and Graduate Attributes
N/A Level
Introductory Level
Intermediate Level
Graduate Level
Professional Level
Advanced Level

Alignment of Assessment Tasks to Learning Outcomes

Assessment Tasks Learning Outcomes
1 2 3 4 5
1 - Written Assessment - 35%
2 - Presentation - 25%
3 - Project (applied) - 40%

Alignment of Graduate Attributes to Learning Outcomes

Graduate Attributes Learning Outcomes
1 2 3 4 5
1 - Knowledge
2 - Communication
3 - Cognitive, technical and creative skills
4 - Research
5 - Self-management
6 - Ethical and Professional Responsibility
7 - Leadership

Alignment of Assessment Tasks to Graduate Attributes

Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7
1 - Written Assessment - 35%
2 - Presentation - 25%
3 - Project (applied) - 40%
Textbooks and Resources

Textbooks

Prescribed

Big Data: Understanding How Data Powers Big Business (2013) latest (2013)

Authors: Schmarzo, Bill
Wiley
Indianapolis Indianapolis , Indiana , USA
ISBN: 978-1-118-73957-0
Binding: Paperback
Prescribed

Business Intelligence and Analytics: Systems for Decision Support 10th Global (2015) 10th (2015)

Authors: Turban , Sharda & Delen
Pearson
Upper Saddle River Upper Saddle River , NJ , USA
ISBN: 9781292009209
Binding: Paperback
Supplementary

Next Generation Databases: NoSQL, NewSQL, and Big Data (2015) ()

Authors: Harrison, Guy
Apress Media
New York City New York City , New York , USA
ISBN: 978-1-4842-1330-8
Binding: Paperback
Supplementary

Scalable Big Data Architecture: A practitioner’s guide to choosing relevant big data architecture (2016) (2016)

Authors: Azarmi, Bahaaldine
Apress Media
New York City, New York City, , New York, , USA
ISBN: 978-1-4842-1327-8
Binding: Paperback

Additional Textbook Information

If you prefer to study with a paper copy, they are available at the CQUni Bookshop here: http://bookshop.cqu.edu.au (search on the Unit code). eBooks are available at the publisher's website.

IT Resources

You will need access to the following IT resources:
  • CQUniversity Student Email
  • Internet
  • Unit Website (Moodle)
  • Oracle VM Virtual Box
  • Hadoop (requires 16 GB RAM)
  • Zoom for teaching staff and students as this unit will be online
Referencing Style

All submissions for this unit must use the referencing style: Harvard (author-date)

For further information, see the Assessment Tasks.

Teaching Contacts
Meena Jha Unit Coordinator
m.jha@cqu.edu.au
Schedule
Week 1 Begin Date: 13 Jul 2020

Module/Topic

1. Introduction to Big Data

2. An Overview of Business Intelligence, Analytics, and Decision Support


Chapter

1. Online Resources
2. Chapter 1 from Business Intelligence and Analytics: Systems for Decision Support.
Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Week 2 Begin Date: 20 Jul 2020

Module/Topic

1. Big Data Business Opportunities

2.
Foundation and Technologies for Decision Making

Chapter

1. Chapter 1 from Big Data: Understanding How Data Powers Big Business. Author: B. Schmarzo
2. Chapter 2 from Business Intelligence and Analytics: Systems for Decision Support.

Authors: R Sharda, D Delen,E Turban


Events and Submissions/Topic

Week 3 Begin Date: 27 Jul 2020

Module/Topic

1. Big Data Technologies: Overview of Hadoop; MapReduce; scripting language

2. Information Management and Business Reporting, Visual Analytics

Chapter

1. Chapter 2 from Next Generation Databases: NoSQL, NewSQL, and Big Data. Author: G. Harrison
2. Chapter 4 from Business Intelligence and Analytics: Systems for Decision Support.

Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Week 4 Begin Date: 03 Aug 2020

Module/Topic

1. Next Generation Databases

2. Predictive Modeling: classification versus regression; evaluating predictive models and cross validation; algorithms for predictive modelling

Chapter

1. Chapter 4, 5 & 6 from Next Generation Databases: NoSQL, NewSQL, and Big Data. Author: G. Harrison
2. Chapter 6 from Business Intelligence and Analytics: Systems for Decision Support.

Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Week 5 Begin Date: 10 Aug 2020

Module/Topic

1. Understanding Value Creation Process

2. Business Analytics, Text Analytics, Text Mining, and Sentiment Analysis

Chapter

1. Chapter 7 from Big Data: Understanding How Data Powers Big Business. Author: B. Schmarzo
2. Chapter 7 from Business Intelligence and Analytics: Systems for Decision Support.

Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Vacation Week Begin Date: 17 Aug 2020

Module/Topic

Enjoy the break.

Chapter


Events and Submissions/Topic

Week 6 Begin Date: 24 Aug 2020

Module/Topic

Web Analytics, Web Mining, and Social Analytics

Chapter

Chapter 8 from Business Intelligence and Analytics: Systems for Decision Support.

Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Assessment 1: Written Assessment Due: Week 6 Friday (28 Aug 2020) 11:45 pm AEST
Week 7 Begin Date: 31 Aug 2020

Module/Topic

Creating the Big Data Strategy

Chapter

Chapter 6 from Big Data: Understanding How Data Powers Big Business.

Author: B. Schmarzo

Events and Submissions/Topic


Week 8 Begin Date: 07 Sep 2020

Module/Topic

Big Data User Experience Ramification


Chapter

Chapter 8 from Big Data: Understanding How Data Powers Big Business.

Author: B. Schmarzo

Events and Submissions/Topic


Week 9 Begin Date: 14 Sep 2020

Module/Topic

Identifying Big Data Use Cases

Chapter

Chapter 9 from Big Data: Understanding How Data Powers Big Business.

Author: B. Schmarzo

Events and Submissions/Topic

Week 10 Begin Date: 21 Sep 2020

Module/Topic

Cloud Computing and Business Intelligence: emerging trends and future impacts of business analytics

Chapter

Chapter 14 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Assessment 2: Presentation Due: Week 10 Friday (25 Sept 2020) 11:45 pm AEST
Week 11 Begin Date: 28 Sep 2020

Module/Topic

Solution Engineering

Chapter

Chapter 10 from Big Data: Understanding How Data Powers Big Business. Author: B. Schmarzo.

Events and Submissions/Topic

Week 12 Begin Date: 05 Oct 2020

Module/Topic

1.Big Data Architectures

2.Big Data Reference Architectures

Chapter

Online Resources

Online Resources

Events and Submissions/Topic

COIT20253 Assessment 3: Practical and Written Assessment Due: Week 12 Friday (9 Oct 2020) 11:45 pm AEST
Review/Exam Week Begin Date: 12 Oct 2020

Module/Topic

No exam for this unit

Chapter

Events and Submissions/Topic

Exam Week Begin Date: 19 Oct 2020

Module/Topic

Chapter

Events and Submissions/Topic

Term Specific Information

Contact information for Meena Jha: Email: m.jha@cqu.edu.au Office: Level 2, 400 Kent Street, Sydney Campus; P +61 2 9324 5776 | X 55776. Please submit questions about the unit through the 'Q&A' discussion forum in Moodle - that way, everyone can benefit from the questions and answers. If you have any individual queries, please email me and I'll try to get back to you within a day or so.

Assessment Tasks

1 Written Assessment

Assessment Title
Assessment 1: Written Assessment

Task Description

COIT20253 Assessment 1: Written Assessment


Due Date
Week 6 Friday 11:45 PM AEST


Weighting:
35%


Assessment Task:

In this assessment, you are required to choose one of the following industries: Healthcare, Insurance, Retailing, Marketing, Finance, Human resources, Manufacturing, Telecommunications, or Travel. This assessment consists of two parts as follows:

Part A - You are required to prepare a report on how Big Data could create opportunities and help value creation process for your chosen industry.

Part B - You need to identify at least one dataset relevant to the industry and describe what opportunities it could create by using this dataset.

In Part A, you will describe what new business insights you could gain from Big Data, how Big Data could help you to optimise your business, how you could leverage Big Data to create new revenue opportunities for your industry, and how you could use Big Data to transform your industry to introduce new services into new markets. Moreover, you will need to elaborate how you can leverage four big data business drivers- structured, unstructured, low latency data and predictive analytics to create value for your industry. You are also required to use Porter’s Value Chain Analysis model and Porter’s Five Forces Analysis model to identify how the four big data business drivers could impact your business initiatives.

In Part B, among several open source and real-life datasets, you will identify at least one dataset that is relevant to the industry you have chosen. The dataset can be a collection of structured, unstructured or semi-structured data. Using this dataset, you will first discuss how you chose this dataset among other datasets. Then, you will identify and present the metadata of the dataset. Using the chosen dataset, you will need to describe the opportunities it could create for the chosen industry.


The length of the report should be around 2500 words. You are required to do an extensive reading of more than 10 articles relevant to Big Data business impacts, opportunities and value creation process. You need to provide in-text referencing of the chosen articles.


Assessment Due Date

Week 6 Friday (28 Aug 2020) 11:45 pm AEST

Assessment 1 is due on Friday of Week 6 at 11:45 PM AEST


Return Date to Students

Week 8 Friday (11 Sept 2020)

Within two weeks of submission


Weighting
35%

Assessment Criteria

You will be assessed based on your ability to analyse and reflect on how organisations are leveraging non-traditional valuable data (unstructured, real-time) with the traditional enterprise data (structured) for business intelligence and value creation. The marking criteria for this assessment are as follows.


Part A (25 marks):
Executive Summary - 3 marks

Table of Contents - 1 mark

Introduction - 2 marks

Big Data Opportunities - 4 marks

Value Creation using Big Data - 4 marks

Porter’s Value Chain Analysis - 4 marks

Porter’s Five Forces Analysis - 3 marks

Conclusion - 2 marks

References - 2 marks


Part B (10 marks):

Dataset identification – 2 marks

Metadata of the chosen dataset – 3 marks

Business opportunities through the chosen dataset – 5 marks


Referencing Style

Submission
Online

Submission Instructions
You must upload the written report to Moodle as a Microsoft Office Word file by the above due date.

Learning Outcomes Assessed
  • Apply concepts and principles of big data to evaluate and explain how large volume of structured and unstructured data are managed in an organisation
  • Analyse critically and reflect on how organisations are including non-traditional valuable data with the traditional enterprise data to do the business intelligence analysis


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Research
  • Self-management
  • Ethical and Professional Responsibility

2 Presentation

Assessment Title
Assessment 2: Presentation

Task Description

COIT20253 Assessment 2: Presentation


Due Date
Week 10 Friday 11:45 PM AEST


Weighting:
25%


Assessment Task:


In this
assessment, you will be presenting your big data strategy document by choosing an application of Big Data in one of the following industries: Healthcare, Insurance, Retailing, Marketing, Finance, Human resources, Manufacturing, Telecommunications, or Travel. You can choose the same industry as your Assessment-1. You will develop a strategy document for the business application of your choice using the dataset that you identified in Assessment-1 and deliver a presentation based on this strategy document. You can choose different dataset to what you have chosen for your assignment 1.

In the first step, you will come up with a targeted business strategy. You will then break down the business strategy into associate key business initiatives, followed by outcomes and critical success factors to support those business initiatives. Afterwards you will identify the specific tasks that need to be executed to support the targeted business initiatives. Next, you will identify the data sources required to support your business initiatives in addition to the datasets that you have chosen in Assessment-1.


Once you have completed the strategy document, you will turn the strategies into actions by identifying the Big Data analytics, business intelligence requirements. You will also specify how the data will be used to gain actionable insights that would help your business initiatives.

The process of creating the strategy document, and turning those strategies into actions, and the outcomes of the process should be clearly illustrated in your presentation.

The presentation will start from week 10 and continue till week 12. The presentation time of all students will be determined by the Unit Coordinator.


Assessment Due Date

Week 10 Friday (25 Sept 2020) 11:45 pm AEST

All presentation slides must be submitted on Moodle in Week 10 Friday at 11:45 PM


Return Date to Students

The assessment marks will be released on certification date.


Weighting
25%

Assessment Criteria

You will be assessed based on your ability to develop Big Data strategies for data-centric organisations to meet client requirements and to apply Big Data architecture, tools, and technologies for decision making and problem solving. The marking criteria for this assessment are as follows.


Demonstrated Understanding of Strategy Document - 6 marks

Turning Strategies into Actions - 6 marks

Clarity, Consistency and Structure of Presentation - 3 marks

Use of Quality References - 3 marks

Visual Aids - 3 marks

Time Management - 2 marks

Quality Response to Questions - 2 marks


Referencing Style

Submission
Online

Submission Instructions
You must upload the presentation file as ppt to Moodle by the above due date.

Learning Outcomes Assessed
  • Develop big data strategy for data-centric organisations to meet client requirements
  • Apply big data architecture, tools, and technologies for decision making and problem solving in the organisational context.


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Research
  • Self-management
  • Ethical and Professional Responsibility

3 Project (applied)

Assessment Title
COIT20253 Assessment 3: Practical and Written Assessment

Task Description

COIT20253 Assessment 3: Practical and Written Assessment


Due Date
Week 12 Friday 11:45 PM AEST


Weighting:
40%


Assessment Task:

This is an individual assessment. In this assessment, you are required to produce a report based on the Big Data strategy document you developed for Assessment-2(Presentation). You also need to analyse the datasets relevant to the business that you identified in Assessment 1 using big data tools and describe how the outputs of these tools could help you to create the Big Data Strategy.


At the beginning of the report, you will identify some Big Data use cases based on the Big Data strategies you developed for Assessment 2. In the following part, you will critically analyse different Big Data technologies, data models, processing architectures and query languages and discuss the strengths and limitations of each of them. You will also discuss different Big Data analytics and business intelligence tools that can be applied on the chosen datasets so businesses can gain actionable insights from Big Data. Moreover, you will discuss the Big Data technologies that you could use for data collection, storage, transformation, processing and analysis to support your use cases.


You will also illustrate the Big Data technology stack and processing architecture required to support your use cases. You have to provide the rationale behind each of the choices you make. Finally, you will specify what user experiences you are going to provide to aid in decision-making. Your target audience is executive business people who have extensive business experience but limited ICT knowledge. Hence, they would like to be informed as to how new Big Data technologies that you have applied on the datasets could benefit their business. Please note that a standard report structure, including an executive summary, must be adhered to.


The main body of the report should include but not limited to the following topics:

1. Big Data Use Cases

2. Critical Analysis of Big Data Technologies

3. Big Data Architecture Solution

The length of the report should be around 3000 words. You are required to do an extensive reading of more than 10 articles relevant to the chosen Big Data use cases, technologies, architectures and data models. You will need to provide in-text referencing of the chosen articles. You assessment report must have a Cover page (Student name, Student Id, Unit Id, Campus, Lecturer and Tutor name) and Table of Content (this should be MS Word generated).


Assessment Due Date

Week 12 Friday (9 Oct 2020) 11:45 pm AEST

The assessment is due on Friday week 12 11:45 AEST.


Return Date to Students

On certification date


Weighting
40%

Assessment Criteria

You will be assessed based on your ability to critically analyse, use and evaluate different Big Data technologies and to apply Big Data architecture, tools, and technologies to support Big Data use cases. The marking criteria for this assessment are as follows.


Executive Summary - 3 marks

Table of Contents - 2 marks

Introduction - 2 marks

Big Data Use Cases - 3 marks

Critical Analysis of Big Data Technologies - 8 marks

Use of Big Data tools on the dataset - 5 marks

Critical analysis on the output - 8 marks

Big Data Architecture Solution - 3 marks

Conclusion - 3 marks

References - 3 marks


Referencing Style

Submission
Online

Submission Instructions
You must upload the written report to Moodle as a Microsoft Office Word file by the above due date.

Learning Outcomes Assessed
  • Critically analyse and evaluate different big data technologies used for decision making in an organisation
  • Apply big data architecture, tools, and technologies for decision making and problem solving in the organisational context.


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Research
  • Self-management
  • Ethical and Professional Responsibility

Academic Integrity Statement

As a CQUniversity student you are expected to act honestly in all aspects of your academic work.

Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.

When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.

Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.

As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.

What is a breach of academic integrity?

A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.

Why is academic integrity important?

A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.

Where can I get assistance?

For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.

What can you do to act with integrity?