COM641 - Distributed Data and Data Analytics Assignment

Assignment Task

Assignment Description

The assignment will entail reading a range of academic papers, journals and books. You will first be writing reports by critically evaluating the adoption of data analytics and the issues. You will then be preparing data visualisations and explaining the information conveyed by the visualisations.

Report on big data platforms

In Assignment 1, you designed a distributed data solution for a cruise company called Happy Cruise Lines. In the last week, a much larger operator of cruise ships called Joyful Cruise Lines has taken over Happy Cruise Lines. A review of the IT systems of the two organisations is now being considered. Joyful Cruise Lines has nearly 100 ships and has served 2.5 million customers. These customers have made nearly 10 million voyages.

As part of the IT review, the development of a big data platform is being considered. The company would like in depth analysis of customer and voyage data to better understand what types of cruises, fares and cities are most popular with different segments of customers (customers could be segmented by gender, age, nationality etc).

Since Joyful Cruise Lines want to use the data the two organisations have for detailed customer analytics, the data may be used to personalise, aggregate, and measure customer preferences and experience. This may necessitate converting data into different formats, and applying various analysis techniques, tools and models.

Your task is to conduct research into big data platforms and produce a report which evaluates the benefits, costs, complexity, security and other relevant aspects the company should be aware of in adopting and implementing a big data platform. If your view is that the company need not implement a big data platform, you should come up with alternative recommendations. In light of the above customer and voyage data analytics requirement, your report should critically evaluate the legal, social and ethical implications in collecting, analysing, and using personal data to create knowledge to support business decision making using the chosen big data / data analytics solution. You should make sure that you support your report with relevant examples and justify your findings and recommendations with appropriate and relevant references. References can be drawn from books, journals and other quality information resources. The IEEE Referencing style must be used.

Data Analysis & Visualisation

For this task, you should use the economic data that has been provided to you. In Moodle, you can find a spreadsheet containing this data.

The spreadsheet provided has two datasets: one is on the output value of different business sectors in different regions of the UK. The other is on employment in those sectors for the four nations that make up the UK.

Your tasks are:

2.1 Data preparation and calculations: In any data analysis / business analytics activity, analysts must usually go through a data preparation stage. Often, raw data is generated in formats that are not immediately amenable to visualisation or comparison. You must consider the two datasets and identify how you can match the two datasets for comparative analysis. Some of the tasks require you to perform additional calculations / format the data differently and/or build a new dataset. If that is necessary, you should do so and submit the revised data and calculations. Calculations can be done using Excel or equivalent.

2.2 Visualisation of data: Using Oracle Analytics Cloud, you have to produce visualisations for the following:

•  GVA by sector for the four nations
•  Employment by sector for the four nations
• Comparative visualisation showing GVA growth and employment growth by sector for each of the four nations
• Choose any five sectors and plot the annual GVA growth in percentage terms for the chosen sectors
• For the same sectors used in 2.2.4, plot the annual percentage growth in employment
• You must consider what type of graph is suitable for each of the above visualisations. As part of the commentary in 2.3 briefly mention why the graph chosen is the appropriate one to easily understand the data. Each visualisation must have relevant titles, labels and description

2.3 Data interpretation and commentary: It is not enough to create graphs and visualisations. Data interpretation and reporting is critical to decision making. You must, therefore, separately provide brief comments about what information is conveyed by the visualisation. The comments must be no more than 75 words per visualisation

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