COIT20253 Assessment 3: Practical and Written Assessment
Due Date: Week 12 Friday (2 June 2023) 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 any big data tools and describe how the
outputs of these tools could help you to create the Big Data Strategy. As you have been taught
Tableau in this unit, you can use Tableau to analyse your dataset. You can include any additional
datasets that would support your big data strategy for example Predictive maintenance that involves
using data analytics to predict when equipment or machinery is likely to fail, allowing organizations
to perform maintenance before a breakdown occurs. This approach can help organizations reduce
downtime, minimize maintenance costs, and extend the life of equipment; or Fraud detection that
involves using data analytics to identify patterns of fraudulent activity. By analysing large volumes
of data, organizations can identify potential fraudulent activity and take action to prevent financial
losses.
As already suggested you need to develop on your Assessment 2.
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.
For example: Big data can be a powerful tool in detecting and preventing fraud. Here are some
common use cases for big data in fraud detection:
• Behavioral analytics: Behavioral analytics involves analyzing user behavior to identify
patterns of fraudulent activity. By collecting and analyzing data on user activity,
organizations can identify abnormal behavior patterns and take action to prevent fraud.
• Machine learning algorithms: Machine learning algorithms can be used to identify
patterns of fraudulent activity based on large volumes of historical data. By analyzing
past fraudulent activity, machine learning algorithms can learn to identify and flag
potential fraudulent activity in real-time.
• Network analysis: Network analysis involves analyzing the relationships between
different entities, such as individuals, accounts, and transactions, to identify patterns of
fraudulent activity. By analyzing these relationships, organizations can identify
suspicious transactions and prevent fraud.
• Real-time monitoring: Real-time monitoring involves monitoring transactions in real-time
to detect and prevent fraudulent activity. By using big data to monitor transactions as they
occur, organizations can identify potential fraud and take immediate action to prevent it.
• Text analytics: Text analytics involves analyzing unstructured data, such as emails and
chat messages, to identify patterns of fraudulent activity. By analyzing text data,
organizations can identify potential fraud and take action to prevent it.
These are just a few examples.
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. A big data technology stack is a set of technologies and tools used to
process, store, and analyze large volumes of data. It includes data storage, data processing,
data integration, data analytics, machine learning, visualization, and cloud platforms. The focus
of the technology stack is on the selection and integration of different tools and technologies to
build a comprehensive big data infrastructure.
Big data processing architecture refers to the overall design and framework of the big data
processing system. It encompasses the end-to-end process of data ingestion, storage,
processing, analysis, and visualization. It involves the selection and configuration of the
different technologies, tools, and processes required to implement the data processing system.
The focus of the processing architecture is on the overall system design and how the different
components interact with each other.
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 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).
Caution: ALL assessments 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 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