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HI6008 Assignment 2 Requirements All HI6008 Students Enrol in the Semester 1/2018 need to follow below Assignment structure: 1. Introduction 2. Project Objective 3. Project Scope 4. Literature Review...

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HI6008 Assignment 2 Requirements

All HI6008 Students Enrol in the Semester 1/2018 need to follow below Assignment
1. Introduction
2. Project Objective
3. Project Scope
4. Literature Review
(Students’ needs to summarise Assignment 1 literature review (2-3 pages) and justification
from Assignment 1 literature problems, gaps opportunities, Hypothesis)
5. Research Questions/Hypothesis
- Primary Question (only one question)
- Secondary Questions (1, 2 ….)
Research questions should be linked to Literature Problems, Gaps, and Hypothesis
6. Research Design and Methodology
- Qualitative research
(Students should propose the Process of the Qualitative Research (Main Steps),
Approaches to reliability and Validity, Sampling, Sample Size, Data Collection
Method, Variables Specifications)
- Quantitative research
(Students should propose the process of the Quantitative Research Design Process
(Main Steps), Research Instrument, Quantitative Data Analysis Process, Sampling and
Simple Size, Interviewing and Questionary Design, Reliability and Validity of Data)
7. Research Limitations
8. Time Schedule (Research plan)
9. Conclusion
10. Reference List
11. Appendix
NOTE: Students are not with requirements to collect and analyse data
Answered Same Day May 20, 2020 HI6008


Sundeep answered on May 24 2020
114 Votes
Big Data in Business Organizations        15
Student Name:
Course ID:
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Table of contents:
Big Data History and Cu
ent Scenario--------------------------------------------4
Big Data for Business-------------------------------------------------------------------6
Big Data Goals---------------------------------------------------------------------------9
Big Data Frameworks-----------------------------------------------------------------10
Big Data Applications-----------------------------------------------------------------11
Big Data Challenges-------------------------------------------------------------------12
Big Data Facts and Figures-----------------------------------------------------------14
When the data on the internet exploded, big data emerged! Since the inception of the digital age, loads of data has been created by us. This data came into use with the advancement of the microprocessors. It burst with the advent of the internet. The rise of computers, the usage of internet, betterment of knowledge and novelty at regular intervals have led to the growth of data into big data. Data helps the companies to keep insights and helps them make decisons. Before the digital age, data was held in physical formats such as papers, files etc. Computers and spreadsheets gave us a responsible and a compact storage to organise data into large scale and in an easily understandable and accessible way. One click of the mouse and the information was in hand! The data created in the year 2000 is similar to the data we created in 2 days!. It is believed that by the year 2020, the total worldwide data would exceed 5zettabytes today to 50zettabytes! i.e. 10 times more! (Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014)
The data we generate has created a user footprint which is digital in nature and can be traced back to the user. We generate data at every step in our lives be it switching on the GPS or even sending a text message. Machines generate data too and the amount of data that is generated by the machines is huge. In smart homes, the devices collect data and share it among the other devices to make it smarter. The data is collected from very server and every node point to make it sequential so that it makes sense. The following report is the analysis and the use of Big data in the industry and its applications
Big Data History and Cu
ent Scenario
Big data is the term coined after the year 2000 when huge amounts of data came to be generated by the use of social media and other internet related things that created the term Big Data. The industry analyst Doug Lanley was the one to articulate the mainstream definition of the Big Data which includes the 3 V’s
Volume: Social media, transactions, signals, traffic, surfing, mobile, laptop and multiple sources are utilised by the machines to collect data and store them. Due to cloud and reduced prices of the storages, storing this data is not at all difficult, Cloud storages and technologies such as Hadoop have eased out data storage.
Velocity: Structured and unstructured data a
ive at the rate ko mb’s and gb’s per second at the servers which has to be stored and kept for analysis tools to gather insights. The unclean data has to be structured and stored in a clean and safe systematic manner. Sensors and smart metering devices to unstructured data, text documents, emails videos and texts all are different kinds of data which a
ive at speed! (Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014)
Variety: The variety of data refers to the difference in the formats of the data that are being used in the machines. Consider an example of image and video, there are more than 10 formats available for them when we want to process them, now consider documents, presentations, music and so much more. When we consider SAS, two dimensions of data are considered:
1. Variability: Variability in the data refers to the quantity of the data that is available at the servers. The increase and the decrease in the velocity of the data is what is measured in variability of data. There are high and low points in the data that a
ives. If something good and popular is on the social media, it would explode and the data streams would be active but in case nothing new is happening, the data consumptions and the data usage would be low
2. Complexity: The data has to be mixed and matched since they come from various sources, cleansed and transformed in order to gather insights. The data connection is important to understand the meaning of the data and if the join doesn’t fit and value cannot be deduced, it would be just unfiltered raw data which has a
Big Data for Business
There are multiple organisations that use data for their...

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