COIT20253 Assignment Help
Practical and Written Assessment Assignment help
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.