Data Mining: Applications, Ethical Implications, and Analysis

Verified

Added on  2020/03/04

|11
|2136
|39
Report
AI Summary
This report on data mining explores its diverse applications across various sectors, including healthcare, banking, and education. It emphasizes the importance of data mining in understanding customer behavior and maintaining data integrity. The report delves into real-world examples, such as Cambridge Analytica, to illustrate how data mining is utilized in political campaigns and the ethical considerations that arise. It analyzes security issues, including the misuse of customer data, privacy concerns, and the impact of big data. The report also examines ethical implications, such as the loss of private employee data and the importance of transparency in data usage. Ultimately, the report concludes that data mining has significant applications across various sectors and that companies must prioritize transparency, data security, and ethical considerations to protect customer privacy and maintain trust.
Document Page
Running head: DATA MINING
DATA MINING
Name of Student
Name of University
Author’s note
tabler-icon-diamond-filled.svg

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
1
DATA MINING
Table of Contents
TASK 1............................................................................................................................................2
Usage of data mining in business....................................................................................................2
Necessity of data mining.................................................................................................................2
Applications of data mining in business..........................................................................................2
Recent articles related to data mining..............................................................................................3
TASK 2............................................................................................................................................5
Introduction......................................................................................................................................5
Analysis...........................................................................................................................................5
Importance of implication................................................................................................................7
Conclusion.......................................................................................................................................8
References........................................................................................................................................9
Document Page
2
DATA MINING
TASK 1
Usage of data mining in business
Necessity of data mining
Data mining is a collection of great data that consist of certain pattern are derived from it
used for studying the behavior of customer in the market. Data Mining is an integral part of the
business for encouraging the customer behavior and purpose to gain the preferences of the
customer towards the company in the market.
Applications of data mining in business
There are various applications of the data mining in companies in the market in different
field. Some of the applications are discussed below:
HealthCare Industry
There are different data and information related to the patients are recorded in the
hospitals. Therefore, data mining is an important element in arranging the data according to the
patent. The data mining helps in maintaining the data related to treatment of a particular patient
in the hospital. It also helps in predicting the outbreaks of the disease.
Banking
There are large amount of data and information are stored in the bank related ti the
customers. There are different types of data including cash amount, customer’s name, address,
shopping bills and other related data. Therefore, it is important to maintain the correct relation of
Document Page
3
DATA MINING
the data with respective customer. The data mining helps in maintaining a huge pool of data and
allocate with appropriate customer.
Education
The data mining has also helped in education sector. Many lectures and classes are
allocated to particular student. The results of a particular student is maintained by the data
mining. The completely academic career of a student is maintained by the use of data mining in
the sector.
Detection of lies and fraud
The data mining helps in creating meaningful security pattern of the data. Therefore, this
can be used in detecting the lies and fraud cases in the organization. There are various program
installed in the data mining process that helps in detecting the lies and fraud.
Recent articles related to data mining
Cambridge Analytical: Trump's data mining advisers to meet Australia's Liberal MPs.
The above article focuses on a data mining company named as Cambridge Analytical that
is a key backroom operatives of the US president Donald Trump’s Campaign in the white house.
The meeting is organized on 6 April 2017 with the Liberal party, parliamentarians and
government staffs. Cambridge Analytica includes controversial “psychographic” methods for
identifying a particular slogan by the voter. This company is also opening its branch in the
Australia (Murphy, 2017).
This article discusses about the use of data mining organizing the meeting in the white
house by the company. The data of the representatives and other staffs have to be properly
tabler-icon-diamond-filled.svg

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
4
DATA MINING
maintaining to ensure the security. Cambridge Analytica has used psychographic methods for
applying various slogans attracting maximum voters.
It can be concluded from this article that the data mining has an important role in
maintaining a proper secured system in the organization. There are various applications of the
data mining including finance, education and health sector. The political backgroung=d of the
data maining has been analysed in the article by the Cambridge Analytica. This idea have
successfully implemented and expanding n the Australia.
Document Page
5
DATA MINING
TASK 2
Introduction
This report discusses about the concept of data mining in the organization. There are
various advantages of the data mining has been discussed in the report in different fields. The
ethical implication of the data mining has been properly discussed in the report. This report
focuses on the use of the data mining in the organization for keeping the privacy of the customer.
Analysis
Security issues
There are various activities performed in an organization that are related to the collection
of huge amount of data and information of the customers in the market therefore, there is a keen
need of the data mining to maintain their data in a formatted manner. There can be misuse of the
data during the formatting process (Big data security problems threaten consumers'
privacy ,2017). This can be dangerous for the company due to the data loss. The application of
the data mining has been limiting the security of the data and information of the customers in the
market.
Size of big data
The big data consists of huge amount of data and information that are collected and
stored in a fashion. The hacker can breach the data and information from the big data. According
to a report of 2014, the Arkansas University have comprised fifty thousand private data of the
student (Wu et al, 2014). The Amazon has used various form of big data in the market using the
Document Page
6
DATA MINING
customer data and information the data and information of the customer can be hacked by the
intruders.
Access control
Various company that protect their data to have a single access point for minimizing the
risk, but the big data deals with huge amount of data and to have single access point for such
huge data is not possible making it vulnerable for breaching. Moreover, the software company
does not take security of data as highest priority to compensate with the time and money (Malik,
Ghazi & Ali, 2012). This example might be seen in a software company hardtop, software has
a basic security features but many big companies uses Hardtop for their corporate data platform,
despite limitation.
Privacy issues
The high security is provided to the customer data on their request. The company
increase the security the company by using different tools like access control, encryption,
intrusion detection or backups (Willis III, 2013). Implementing these securities in the company
helps in demanding more private information of customer and make their data more secure. N
case of any breach in the data in place of taking responsibility of breaches, they treat customer as
a potential hacker who can pose threat to the security, even though the agency has sufficient
information that customer is not the terrorist it still makes more decrypted version of their data.
Big data usage
The big data concept has been prevailing in the cimoany for many years that have helped
in maintaining the data and information of the customers in the market. The comoanies focuses
tabler-icon-diamond-filled.svg

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
7
DATA MINING
on utilizing the track of the big data and analysis the importance if the data and information of
the customer (ElAtia, Ipperciel & Hammad, 2012). The company have to claim for using thes big
data for the develooment of the cimian inbthe market. There are various range of customers in
the market that have their account in the company. The company uses this data and information
for making online experience in the market.
Big data, Human Rights and ethics of scientific research
The digital phase has conquered every sector in the modern world that implements
various technologies with the help of computers. The online data can be stored as a wastage of
memory that yield in the one server. This online storage helps in storing the data and information
in the company. The big data concept has proved extremely useful for all sectors of country as
discussed in above article about data mining is useful in election campaign (Australian
Broadcasting Corporation). 2017). On other hand through the incident of the Snowden
revelations common people understand the extent of government surveillance on individual,
misusing application integrated with big data which has compromised the privacy and failed trust
in them (Uzar, 2014) . The cybercrime and hacking news has created fear among individuals and
made the digital world more vulnerable to hacking.
Ethical implication
The ethical implication of the data mining has different approach towards the goals of the
company in the market. A well-recognized issue is the loss of the private data of the employee in
the company that helps in missing the goals. The data mining helps in increasing the sales of the
product by analyzing the product in a proper fashion (Sharma & Panigrahi, 2013). The company
might not accept these conditions and settle the disagreements of the customers in the market.
Document Page
8
DATA MINING
The ethical consideration of the customers helps in maintaining the proper brand image of the
company in the market.
Importance of implication
The prior issues related to the data mining includes misuse of data of customer for
marketing the integrity of products in the market. (Siemens & d Baker, 2012). The customer has
right to sue the company if the privacy is compromised. It is the duty of company to maintain the
transparency of the usage of big data and in case of any breach in its data then it should take the
responsibility for loss.
Conclusion
It can be concluded that data mining has many application in different sectors including
education and healthcare. The article discussed in the report throws light on the concept of the
data mining in the organization. The duty of the company has maintained transparency of use of
big data in the company. The breach in the data is the loss of the company due to ill structured
data mining.
Document Page
9
DATA MINING
References
Murphy, K. (2017). Cambridge Analytica: Trump's data mining advisers to meet Australia's
Liberal MPs. the Guardian. Retrieved 12 August 2017, from
https://www.theguardian.com/australia-news/2017/apr/05/donald-trumps-data-mining-
advisers-to-meet-liberal-mps-in-canberraMiner, G. (2012). Practical text mining and
statistical analysis for non-structured text data applications. Academic Press.
Big data security problems threaten consumers' privacy. (2017). The Conversation. Retrieved 12
August 2017, from https://theconversation.com/big-data-security-problems-threaten-
consumers-privacy-54798
Big Data, Human Rights and the Ethics of Scientific Research – Opinion – ABC Religion &
Ethics (Australian Broadcasting Corporation). (2017). Abc.net.au. Retrieved 12 August
2017, from http://www.abc.net.au/religion/articles/2016/11/30/4584324.htm
Sharma, A., & Panigrahi, P. K. (2013). A review of financial accounting fraud detection based
on data mining techniques. arXiv preprint arXiv:1309.3944.
Uzar, C. (2014). The Usage of Data Mining Technology in Financial Information System: An
Application on Borsa Istanbul. International Journal of Finance & Banking Studies, 3(1),
51.
Strohmeier, S., & Piazza, F. (2013). Domain driven data mining in human resource management:
A review of current research. Expert Systems with Applications, 40(7), 2410-2420.
Willis III, J. E. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause
Review Online.
tabler-icon-diamond-filled.svg

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
10
DATA MINING
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions
on knowledge and data engineering, 26(1), 97-107.
ElAtia, S., Ipperciel, D., & Hammad, A. (2012). Implications and challenges to using data
mining in educational research in the Canadian context. Canadian journal of
education, 35(2), 101.
Siemens, G., & d Baker, R. S. (2012, April). Learning analytics and educational data mining:
towards communication and collaboration. In Proceedings of the 2nd international
conference on learning analytics and knowledge (pp. 252-254). ACM.
Malik, M. B., Ghazi, M. A., & Ali, R. (2012, November). Privacy preserving data mining
techniques: current scenario and future prospects. In Computer and Communication
Technology (ICCCT), 2012 Third International Conference on (pp. 26-32). IEEE.
chevron_up_icon
1 out of 11
circle_padding
hide_on_mobile
zoom_out_icon
[object Object]