Business Intelligence using Big Data Assessment item 1—Assignment 1Specifications 1 1...

Question

Business Intelligence using Big Data
    Assessment item 1—Assignment 1Specifications
    1
    1
    1
    Due date:
    Week 6 Sunday (16 Apr XXXXXXXXXX:45 pm AEST.
All students are to submit electronically – max file size is 2Mb.
    ASSESSMENT
    Weighting:
    35%

    Length:     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 business operations based on the collection, extraction, and analysis of business data to improve decision has been 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

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 business. 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 readings. 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 relevant 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
· 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

tae_636593221359236143_116911_1.docx

Amit · Accepted Answer

Title of the assignment:
Student name:
Professor name:
Course title:
Date:
Table of content
	Serial number
	Task name
	Page number
	1.
	Executive summary
	3
	2.
	Report introduction
	3
	3.
	Data Collection and Storage
	3
	4.
	Data in Action
	7
	5.
	Business continuity
	10
	6.
	Recommendations & Conclusion
	11
	7.
	References
	12
Executive summary
For completing this study work, the first topic named “Data for Supply Chain and Operations Management” is selected by me. The PTC system for making data collections for big data and performing analysis is introduced. The data collection process is being mentioned with respect to PTC systems. The required processes for data storage with related methods and showing importance are also being described in this study work. The centric design for consumer products and possible recommendations is discussed with respect to possible occurred problems which may be faced during any disaster condition for any online business idea. The possible recommendations to faced problems are also introduced in my study work. 
Report introduction 
The data set of very large and possibly complex data is considered as big data. The traditional database systems are not able to make analysis and performing calculations for very large and possibly complex data sets. The big data provides different factors which can be used to capture and making search of any data element, storage of data sets, analysis of data sets, database sharing, data transfer, implementing virtualization and creation or update operation for the privacy status of the database and processed information. In this produced study report, the data storage and related collection methods with relation to operational management and supply chain are being discussed in a step by step manner. 
Data Collection and Storage 
The PTC system is very commonly used for the data collection purpose in most of the organizations. The efficient data collection process from all the possible data suppliers makes it most recommended system of data collection. This system provides different methods which allow the automatic generation and submission of supplier data directly to the manufacturer on bases of different industry standards.  The PTC system helps the organizations to process the specific data justifying any customer requirements in a dedicated format. The organizations can use PTC systems to validate and aggregate the responses from data suppliers. The third party tools included by this system includes the functionalities of detailed information for any particular part, required material, and possible compliances in detailed manner. The data collection is performed on bases of design specifications of data supplier to this PTC system. The PTC systems also help in validating and performing normalization for the entire content load from data supplier. The data collection process helps the organization to produce the required reports with functions of forecasting methods for predicting trends of future market and tracking the latest trends [Brandenburg et al, 2014]. The used methods by big data for making data collection and its formulating are described below: 
1. Advance analytics: The big data specific process for data transfer in place of making use of appropriate already informed decisions is implemented in this data collection method. By implementing the advance analytics for data collection, the overlapping analytics are also been observed. These overlapping analytics are based on classifications of possibly accepted different analytics types. 
2. Descriptive analytics: The past activities of any business process and occurred situations are mainly used for data collection in this analytics method. This method makes use of different patterns, past trends, and related exceptional cases which are already being predicted.  The dash board and reporting standards are utilized to make query execution and producing shelf packages. The alerts for stored data are also provided by this analytics method. By using these alerts, the organization can take necessary and required steps to maintain continuity of supply chain. 
3. Predictive analytics: The analytics method which makes analysis and data comparison on bases of real time for making analysis of historical data is known as predictive analytics of data collection in big data. The future trends are used to make predictions of future probabilities. This method is mostly used in different measures of marketing through SCM for determining the safety and total sales of the stock. To learn the regression process and other statistical algorithm, this is best suited method of data analysis. 
4. Prescriptive analytics: The method which makes use of certain predictions which are based on steps of collected data sets for taking advantages of some user specific requirement and for avoiding any possible situation of data analysis is known as prescriptive analytics. The use of data simulation and possible optimization implementation are main analytics activities performed in this method. 
In today’s supply chain world of data, the increased challenges related to data structure selection and implementation methods increased the coping or handling responsibilities for the organizations. The data collection is very critical activity but storage of collected data and providing its supply on requirements is main challenge for modern organizations. The ERD systems of different business orders, occurred shipment orders, consumer behavior for shopping activities are mainly used to perform data collection. The modern organizations also use channels of social media and GPS to collect the required data from their consumers.  
The major and very important prerequisite for storing the big data is its maintenance for complex and large data sets.

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

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