COIT20253 Business Intelligence using Big Data – Assignment 1- Written Assessment

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Subject Code: COIT20253

Subject Name: COIT20253 Business Intelligence using Big Data

Due Date: Friday, 24 April  2020, 11:45 PM AEST
Weighting: 35%
Assessment Task:
This is an individual assessment.

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 had 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 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 chosen articles.

Your target audience is executive business people who have extensive business experience but limited ICT knowledge. They would like to be informed as to how new Big Data technologies might be beneficial to 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 Opportunities
2.         Value Creation using Big Data
3.         Porter’s Value Chain Analysis
4.         Porter’s Five Forces Analysis

The length of the report should be around 2500 words. You are required to do extensive reading of more than 10 articles relevant to Big Data business impacts, opportunities, and value creation processes. You need to provide in-text referencing of chosen articles.  Your report must have a Cover page (Student name, Student Id, Unit Id, Campus, Lecturer and Tutor name) and a Table of Contents (this should be MS word
generated).
Caution: ALL assessment submissions will be checked for plagiarism by Turnitin.

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

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

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