Data Governance and Data Management Assignment Solution - University

This assignment solution addresses key concepts in data governance and data management. It defines terminologies such as machine-to-machine technology, big data analytics, cloud computing, occupational fraud, botnets, spear phishing, social engineering, and BYOD. The solution also differentiates between 3G, 4G, and 5G technologies, highlighting their characteristics and advancements. Furthermore, the assignment analyzes the Nexus 7 data mining system used by the U.S. military, discussing its purpose, data sources, and benefits. The solution explores how data mining supports the Information Innovation Office (I2O) in achieving its mission, providing a comprehensive overview of data governance and its applications.

Running head: DATA GOVERNANCE AND DATA MANAGEMENT
Data Governance and Data Management
Name of the Student
Name of the University
Author Note

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Question 1: Concept of the Following Terminologies
Machine to machine technology:
The machine to machine technology is considered as a broad label which is used for
describing other technologies which exchanges information via network devices and is able
to perform various actions without any type of help from humans and any type of manual
assistance. This machine to machine technology was first initiated within the manufacturing
industry where other technologies are also functional such as remote monitoring and SCADA
that are very much useful in the cases of remotely control and manage data from equipments
(Hasan, Hossain & Niyato, 2013). This technology is used for tapping the sensor data and
transmitting it to a network.
Big Data Analytic:
The big data analytics is considered as the process of large and varied dataset
examination which are often complex in nature. The main purpose of examining this large
dataset is to discover various types of information which includes unknown correlations,
customer preferences, market trends and hidden patterns that assists businesses and
organization to take proper decisions (Kambatla et al., 2014). In big data NoSQL and hadoop
clusters are primarily as staging areas and landing pads for the data before it pushed into the
data warehouse for the analysis purpose.
Cloud Computing and Cloud Security:
The cloud computing is considered as the availability of the resources regarding
computer system as per the demand of users (Rittinghouse & Ransome, 2016). This is also
considered as the delivering hosted services over the internet. The service of the cloud
computing divided into three types of categories. These are the Platform as a Service,
Infrastructure as a service and Software as a service.

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The cloud security is the process of protecting the online stored data from leakage,
theft and deletion. The main methods of providing the cloud security are penetration
techniques, firewalls and VPN services.
Occupational Fraud:
The occupational fraud is a fraud that is committed against employers by the
employees. In this type of cases employees commits the fraud by utilising his/her job role or
post in the organization for some personal gains which includes utilization of organizational
assets, property and resources inappropriately.
Botnets and Spear Phishing:
The botnets is considered as a number of internet connected devices and each of this
botnet are running one or more than one bots (Silva et al., 2013). Botnets can be used for
various type of attacks including DDoS attack, spamming and for stealing data.
Spear spoofing is a typical type of email spoofing attack which targets some specific
individual or organization for accessing some sensitive data in an unauthorised way.
Social Engineering and BYOD:
The social engineering is a context of information security which refers psychological
manipulation of the peoples for divulging sensitive and confidential information (Atkins &
Huang, 2013).
The BYOD is the synonym of Bring Your Own Device which is also known as bring
your own technology. This is the part of consumerization of IT in which employees of the
organisation are increasingly integrating themselves with their mobile devices that can be
used for organizational purposes (Ghosh, Gajar & Rai, 2013). The policy of BYOD designed

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for controlling the use of employees’ personal devices that are becoming very much
important for mitigating BYOD related risks.
Question 2: Differences among 3G, 4G, and 5G
3G 4G 5G
The 3G is the third
generation wireless
communication technology
that provide email, web
browsing, media sharing and
other smartphone
technologies.
The 4G technology is the
fourth generation wireless
communication technology
which is capable of sharing
network resources for
supporting more connections
simultaneously within the
cell. This is a IP based
network system which
seamlessly maintains voice
and data.
The 5G is currently in the
testing phase and not much
data is available about fifth
generation cellular mobile
networks (Gohil, Modi &
Patel, 2013). Still it has been
assessed that the 5G will
have low latency, reduced
costs, higher capacity and
massive device connectivity.
The implementation of the
5G will also help in the IoT
market.
The speed of the 3G
communication is around 2
megabits per second.
The speed of the 4G
connection is resides within
100 megabits per second to
1 gigabit per second.
The average speed of the 5G
is expected to be within 1
gigabit per second to 10
gigabits per second.
3G technology was firstly
introduced in the year of
2001.
The 4G technology was
introduced in 2010.
The 5G is currently in the
testing phase and expected
to be commercially available

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by 2020.
Question 3:
Nexus 7:
The Nexus 7 is a huge data mining system that has been US military in the region of
Afghanistan to properly understand the society of Afghanistan (Turban, Volonino & Wood,
2015). The main purpose of the Nexus 7 was determining the sign of instability or weakness.
As per the DARPA, Nexus 7 is a breakthrough analysis tool that can describe working area of
a town and the main vulnerability of it. Additionally, it can also determine where the traffic
of the town flows free and where it piles up.
Use of data mining in I2O:
The I2O or the Information Innovation Office is one of the offices of DARPA. The
main aim of the Information Innovation Office is ensuring technological superiority of US in
all the areas where information can be helpful for them to provide a military advantage for
the US (Turban, Volonino & Wood, 2015). In this case the data mining helped the
Information Innovation Office by extracting important data and patterns from the data which
has been collected from Nexus 7. Analysis and data extraction can be done through data
mining.
Data Sources of Nexus 7:
The main data sources of the Nexus 7 is the societies of Afghanistan. One part of the
Nexus 7 group is currently collecting data from the Kabul of Afghanistan. The other group of
the Nexus 7 is working Virginia with a large scale capacity of processing.

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Benefits provided by the Nexus 7:
The Nexus 7 is currently very much important for the DARPA. As per the DARPA
Nexus 7 is revolutionary and far reaching. The main benefit provided by the Nexus 7 is that it
takes data from the various of agencies and produces intelligence regarding culture and
population centric (Turban, Volonino & Wood, 2015). The Nexus 7 also provided the benefit
of figuring out which communities in Afghanistan are stabilizing and which are falling apart.
It also determined which communities are loyal to the government.

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References:
Atkins, B., & Huang, W. (2013). A study of social engineering in online frauds. Open
Journal of Social Sciences, 1(03), 23.
Ghosh, A., Gajar, P. K., & Rai, S. (2013). Bring your own device (BYOD): Security risks and
mitigating strategies. Journal of Global Research in Computer Science, 4(4), 62-70.
Gohil, A., Modi, H., & Patel, S. K. (2013, March). 5G technology of mobile communication:
A survey. In 2013 international conference on intelligent systems and signal
processing (ISSP) (pp. 288-292). IEEE.
Hasan, M., Hossain, E., & Niyato, D. (2013). Random access for machine-to-machine
communication in LTE-advanced networks: Issues and approaches. IEEE
communications Magazine, 51(6), 86-93.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data
analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.
Rittinghouse, J. W., & Ransome, J. F. (2016). Cloud computing: implementation,
management, and security. CRC press.
Silva, S. S., Silva, R. M., Pinto, R. C., & Salles, R. M. (2013). Botnets: A survey. Computer
Networks, 57(2), 378-403.
Turban, E., Volonino, L., & Wood, G. R. (2015). Information technology for management:
Digital strategies for insight, action, and sustainable performance. Wiley Publishing.