COIT20253 - Assessment 1: Written Assessment

Assessment Task:

Assignment 1 is an individual assessment. In this assessment, you are assigned tasks which assess your unit knowledge gained between weeks 1 and 5 about big data and how it can be used for decision making in any industry. All students will have to write a “professional” business report with Executive summary, Table of Content (MS generated); Introduction; Discussion; Conclusion; Recommendations and References.

Please note that ALL submissions will be checked by a computerised copy detection system

and it is extremely easy for teaching staff to identify copied or otherwise plagiarised work.

• Copying (plagiarism) can incur penalties, ranging from deduction of marks to failing the unit or even exclusion from the University.
• Please ensure you are familiar with the Academic Misconduct Procedures. As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure.

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 professional report on WHY Big Data should be integrated to any business to create opportunities and help value creation process for your chosen industry.

Part B - You need to identify at least one open dataset relevant to the industry and describe what opportunities it could create by using this dataset. You can access open data source from different websites. Please try finding it using Google.

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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 why 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 reference of the 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