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1 COIT 20253 Business Intelligence using Big Data Assessment item 1—Assignment 1Specifications Due date: Week 6 Monday (16 Apr XXXXXXXXXX:45 pm AEST. All students are to submit electronically – max...

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COIT 20253 Business Intelligence using Big Data
Assessment item 1—Assignment 1Specifications
Due date: Week 6 Monday (16 Apr XXXXXXXXXX:45 pm AEST.
All students are to submit electronically – max file size is
2Mb.
ASSESSMENT
Weighting:
Length:
35%
The length of the assignment is 3000 words. 1

Objectives

This assessment item relates to course learning outcomes numbers 1, 2, 3 and 4 as stated on the
unit profile.
Assessment 1 is an individual assessment. In assessment 1, you are assigned tasks which assess your
course knowledge gained between weeks 1 and 5 about different facets of Big Data solutions. All
students will have to write a report showing the answers to the tasks 1-3 below.
Please note that ALL submitted A1-reports are passed through a computerized 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 course or even exclusion from the University.

• Please ensure you are familiar with the Academic Misconduct Procedures,
available from: http:
policy.cqu.edu.au/Policy/policy_file.do?policyid=1244
Assignment 1:
The assignment will be marked out of a total of 100 marks and forms 35% of the total assessment for the
course. ALL assignments will be checked for plagiarism by Turnitin.
The ability of Business Intelligence (BI) technologies to provide historical, cu
ent, and predictive views of
usiness operations based on the collection, extraction, and analysis of business data to improve decision has
een the basis of several studies. More recently, “Big Data” and “Big Data Analytics” have further sti
ed
the interest of researchers and practitioners alike. You have been requested to prepare a report focusing on
one of the following topics:
1. Big Data for Supply Chain and Operations Management
2. Sports Analytics
3. Agricultural Analytics
4. Fraud Detection in Banking Sector
5. Big Data for Sentiment Analysis
http:
policy.cqu.edu.au/Policy/policy_file.do?policyid=1244
2
The report should be well researched and written in accordance with Harvard referencing style. The
assignment will be marked out of a total of 100 marks and forms 35% of the total assessment for the unit.
ALL assignments will be checked for plagiarism by Turnitin. You have been requested to prepare a report.
Your target audience is business executives, who have extensive business experience but limited ICT
knowledge. They would like to be informed as to how new Big Data technologies may be beneficial for their
usiness. Please note that standard report structure, including an executive summary, must be adhered to.
The main body of the report should include the following topics.
1. Data Collection and Storage
Data collection system (what kind of data should be collected and how)
Storage system (what are the requirements of the storage and how to achieve them)
2. Data in Action
Consumer-centric product design (what is it and how to do it)
Recommendation system (what is it and how to do it)
3. Business continuity
How online business can survive in case of power outage or other disasters?
The length of the assignment is 3000 words. You are required to do extensive reading of more than 10
appropriate and relevant chosen topics in Big Data application. Please do in-text referencing of all chosen
eadings. Newspaper and magazine reports should be limited to a maximum of 2. A comprehensive report
covering all key aspects of the topic selected is required. Report should be extremely well supported with
elevant case studies. Any assumptions made are clearly noted. DO NOT use Wikipedia as a reference. The
use of unqualified references will result in the deduction of marks.
The report structure should be clear, easy to read and logical, directly addressing the questions. Suitable
headers should be used throughout the report. Good use of graphics and charts should be made.
No spelling, punctuation or grammatical e
ors. The main body of the report should include the following
topics.
Executive Summary: You are required to provide
ief but comprehensive synopsis of your proposal,
which highlights its key points.
Introduction: You are required to introduce the topic of the report in an extremely engaging manner which
arouses the reader's interest. You are required to give a detailed general background that indicates the overall
“plan” of the report.
Discussion of topics: You are required to discuss the following topics in depth. Your discussion should
display deep analysis of issues with no i
elevant info.
Task 1. Data Collection and Storage

• Data collection system (what kind of data should be collected and how)

• Storage system (what are the requirements to the storage and how to achieve
them) Task 2. Data in Action
3
• Consumer-centric product design (what is it and how to do it)

• Recommendation system (what is it and how to do
it) Task 3. Business continuity
• How online business can survive in case of power outage or other disasters?

Conclusion and Recommendations: You are required to provide a well written summary of the main
points and excellent final comment on the subject, provide recommendations based on the information
provided.
Do not write report in essay form.
Submission of Assessments
Reports are to be written in size 12 Arial Font and double spaced. The assignment is to be submitted as
one-word file (.doc) using the electronic assignment submission system that can be accessed from the
link on the course website. Do not submit pdf or any other format.

Assessment Criteria

Assessment Marking Criteria: Weighted out of 35%

1. Report formatting (font, header and footer, table of content, numbering, referencing) 5 Marks

2. Professional communication (co
ect spelling, grammar, formal business language used) 5 Marks

3. Executive summary 10 Marks

4. Report introduction 10 Marks

5. Data Collection and Storage 20 Marks

6. Data in Action 30 Marks

7. Business continuity 10 Marks

8. Conclusion and Recommendations 10 Marks
Total = 100.00
    Assessment item 1—Assignment 1Specifications
    This assessment item relates to course learning outcomes numbers 1, 2, 3 and 4 as stated on the unit profile.
    Submission of Assessments
    Assessment Criteria
    Assessment Marking Criteria: Weighted out of 35%
Answered Same Day Apr 17, 2020 COIT20253 Central Queensland University

Solution

Amit answered on Apr 19 2020
133 Votes
Assignment title: Big data for Supply Chain and Operations Management
Name of Student:
Name of Professor:
Title of course:
Submission Date:
Content table
    Sr. No.
    Name of performed task
    Pg. No.
    1.
    Executive summary
    3
    2.
    Introduction
    3
    3.
    Performing collection with effective storage of data
    4
    4.
    Data for performing desired actions
    7
    5.
    Continuity of business through supply chain and big data
    10
    6.
    Conclusion and possible recommendations
    12
    7.
    References
    13
Executive summary
The given study work has five different topic listed but all of them are related to data collection and performing analysis for big data. Thus, to complete this assignment, the “Data for Supply Chain and Operations Management” is being decided to work on here. For performing the required data collection, the system named “PTC system” is being introduced here. The required processes and strategies for making data collection are described below. The processes required to perform the activities of data storage with their relative working importance are also described in proposed potential work of my study [Kim et al, 2014]. The customer centric design of selected products with probable suggestions for facing any occu
ed issues by any possible disaster for some business idea are also introduced here. The ideas to face the occu
ed issues are also represented here.
Introduction
The implementation of big data shows the use of complex natured large data set to any analysis work. In past, traditional databases are used for small and large size data sets, but with large size data set, their working capabilities are compromised. To overcome such issues and performing better and accurate results for consumers, BIG DATA is being introduced. The functions like searching required data element in large data set, sharing of developed database among different users, transfer of data set from one database to any other, use of virtualization on big data and performing updating of database status are main functions of big data [Zhong et al, 2016]. The developed report of my study work, show and describes the methods of data collection and data storage in any operational supply chain management system. These methods also show the importance of big data in any online e-business activity.
Performing collection with effective storage of data
Most of the organizations which are working in field of e-business and regularly perform data collection activities make use of “PTC system” for their desired task sets. The process used for performing data collection is very efficient to all data suppliers in PTC systems. The PTC system provides various data collection methods through which the organizations are able to perform automatic creation and submitting of all data from suppliers unswervingly in the dataset of manufacturer. The methods used in PTC system are developed according to possible standards of modern industry [Chen et al, 2014]. The fanatical data format which justifies the requirements of any individual is being used by this system. By help of this fanatical data format, only the specific data is being processed by organizations. This system can also be used by different organizations for the response aggregation and validation for any supplier data. The PTC system also make use of some third party applications for providing the functionalities of possible compliances, customer requested material, and to retrieve the detailed and specific information on any specific data portion. The design specifications developed by the supplier of the data are used for performing data collections through the PTC system. The organizations can also perform the normalization activities and data validation activities with help of PTC system. The reports generated by the help of PTC system can be used by organizations for tracking cu
ent and future trends of potential market. For generating such reports, the forecasting methods of data collection are being introduced by this system. The data collection methods of this system for implementing big data are explained here:
1. Advance analytics: It is most commonly used data collection method which use specific data transfer processes instead of some previously taken decisions. To observe the overlapping analytics occu
ed during any data collection, this method is best suited. The possible use of unusual analytics methods is main cause of these overlapping analytics.
2. Descriptive analytics: The business activities which are used in past by any organization and situations which may occur due to these past activities are majorly used to perform the data collection in this analytics method. The past trends, possible patterns and already predicted exceptional cases are being used in this data collection method. The developed standards for producing reports through dash board and packages for shelf are used to execute any customer query in this analytics method. This method provides data alerts, so that the organization can take required necessary steps for maintaining the supply chain stability [Dou et al, 2015].
3. Predictive analytics: The historical data is used for making required comparisons and to develop the possible analysis on real time bases in this analytics method. The predictions for probabilities which may occur in near future are defined by the future trends of this method. The SCM and predictive analytics collectively used for determining all the safety measures of collected data base. The implementation of statistical algorithms with effective use of regression process makes it most useful method for performing data analysis.
4. Prescriptive analytics: In this analysis method, the specific predictions developed on bases of data collections to take advantage from desired requirements of any consumer to avoid any issue are being utilized. The big data optimization and possible simulation of collected data are primary activities which are performed in prescriptive analytics method of data collection.
The maintenance of collected data set of large size and complex nature is always considered as the main precondition [Shi & Abdel-Aty, 2015]. The data growth maintenance in user specific manner of storage is most challenging analytics task for any big data implementation. The input/ output operations which are performed by the consumer are main responsible factors for affecting any...
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