Data Mining: Applications in Business, Security, Privacy, and Ethics

This report delves into the multifaceted world of data mining, exploring its crucial role in various business sectors, from banking and education to healthcare and political campaigns. It highlights the significance of data mining in understanding customer behavior, predicting outcomes, and detecting fraud, referencing real-world examples like Cambridge Analytica's use of psychographic methods. The report then examines the major security and privacy problems associated with data mining, including the challenges posed by the sheer size of big data, difficulties in access control, and the ethical implications of utilizing customer data. It discusses the importance of maintaining transparency and accountability in data usage, emphasizing the need for companies to take responsibility for data breaches and protect customer privacy. The report concludes by underscoring the vast scope of data mining applications and the ongoing need for responsible data management practices to mitigate potential risks and uphold ethical standards.

Running head: DATA MINING
DATA MINING
Name of Student
Name of University
Author’s note

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TASK 1
Usage of data mining in the business
Importance of data mining
Data mining is simply a collection of the huge data from which certain pattern can be
derived which help in the study of the customer behavior towards the company. Therefore the
data mining is important for the business purpose as it help to gain the insight what are the
customer preferences.
Usage of data mining in business
Data mining has huge application in the different business sectors some of them listed
are:
Banking
In the banking sector huge amount of data is recorded everyday in terms of the account
details, shopping bills data, customer’s name, address and various other forms of data. In order to
maintain such a huge pool of data and decode them to derive some information which can help
them to customers shopping pattern data mining application is used.
Education
In the education sector the data mining application is used to predict the results of the
student by studying their learning behavior through the exams data. These data are analyzed by
the coaching institute.

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Healthcare business
With the help of the data recorded in the hospitals, data mining is used to predict the best
medicine practices for the particular disease. Data mining is also used to predict the disease
outbreaks.
For the detection of the fraud and lies
The purpose of the data mining is to derive the meaningful pattern from the data. It can
be used to detect fraud and lies; through the data mining if the programmed meaningful data is
not valid it will term it as invalid thus detecting it as a fraud.
Recent article/news item relating to data mining business
Cambridge Analytica: Trump's data mining advisers to meet Australia's Liberal MPs.
The following article discusses about the data mining company Cambridge Analytical
which is one of the key backroom operatives of the US president Donald Trump’s Campaign for
the white house will meet him 6th of April 2017 with the representatives of the Liberal party,
government staff and parliamentarians, including the veterans (Murphy, 2017). Cambridge
Analytica is famous for its usage of the controversial “psychographic” methods to identify which
particular slogan is best to persuade the voter. This particular company is going to set its
organization in the Australia.
The following article discusses the application of the data mining how an American
company Cambridge Analytica has the used the psychographic methods to study the various
slogans which is the best to attract the maximum voters. Now they are going to the same in
Australia for which they are establishing their company in Australia.

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Thus from the article it can be concluded that the data mining applications is not only
limited to the sector like education, finance or health sector but is also used for the political
purpose by a company called Cambridge Analytical to study the behavior of the customers and
find out the best slogan in order to attract them. The idea has worked their successfully and they
are expanding their business in Australia. Thus data mining has vast scope whose application is
endless.

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Task 2
Introduction
The following report discusses the concept of the data mining and the related major
security problems in data mining, privacy problems related to the data mining, the ethical
implication related to the data mining. It further discusses about the importance of this
implication in the business and finally concludes how effectively the company should use the
data mining in keeping the mind the privacy of its customer.
Analysis
The major security issues related to the data mining
Every activity in the business which is performed it take the help of the computer as the
result huge amount of the data is stored and the misuse of these data can compromise the
customer privacy (Big data security problems threaten consumers' privacy ,2017). The
application of the big data is huge like predicting the result before two to three days of its
occurrence or studying the customer behavior (Wu et al, 2014). Following is the some of the
threats which can be exposed with the application of the data mining.
The size of the big data
The big data contains huge data and to protect and preserve these data itself is the huge
challenge. If any hacker is able to breach the data of the company it can put thousands of
customer data at risk (ElAtia, Ipperciel & Hammad, 2012). A report in 2014 of the breaching of
the Arkansas University has compromised around fifty thousand student private data and in the
same year e-commerce giant e-bay has compromised over two hundred million customer’s

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data .The Amazon, in order to protect the data it distributing its data to twelve of its data centers
in the world to minimize the effect.
The access control difficulty
Various company in order to protect their data they prefer to have a single access point ti
minimize the risk, but in the case of the big data it deals with the huge amount of data and to
have a single access point for such huge data is practically not possible making it vulnerable to
breaching. Moreover the software company does not take security of its data as high priority as it
can cost them time and money (Malik, Ghazi & Ali, 2012). The example can be seen in a
software company hardtop, software has a very basic security features but many big companies
uses Hardtop as their corporate data platform, despite its limitation.
The privacy issues in the data mining
Privacy in place of security
To provide the high security to the customer data on their request. The company
in order to increase the security the company uses various tools like access control, encryption,
intrusion detection or backups (Willis III, 2013). To implement these security the company
demands more private information to the customer to make their data more secure. If there is any
breach in the data in place of taking the responsibility of the breaches they treat customer as a
potential hacker who can pose threat to their security, even though the agency has sufficient
information that a particular customer is not the terrorist it still makes more decrypted version of
their data.
The big data usage

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How the companies are utilizing the technology big data also raises the great concern.
The companies are utilizing the big data’s data to track the online move of the customer to study
their choices and the big data companies are helping them to achieve that by providing the
private data to these company (Strohmeier & Piazza, 2013). The company can claim that they are
using these data to make an online experience more friendly but the same will be disagreed by
the customers.
The Big Data, Human rights and the ethics of scientific research
The world is going through the digital phase where almost all the operations are
implemented with the help of computers and it is still going through huge transformation to
make the task simpler and achieve the impossible task which was never done before (Big Data,
Human Rights and the Ethics of Scientific Research – Opinion – ABC Religion & Ethics
(Australian Broadcasting Corporation). 2017). The online data which is stored as a waste can be
analyzed to yield knowledge. On one hand the big data has proved to be extremely useful in all
the sectors of the country as seen in above article how data mining is useful in election
campaign, but on other hand through the incident of the Snowden revelations the common
people came to know the extent of the government surveillance on the people, misusing the
application of the big data which has has not only compromised the privacy but also failed the
trust in them (Uzar, 2014) . The cybercrime and hacking news has created the fears among
people and made the digital world more vulnerable to hacking.
Ethical implication in data mining
The main intent of using data mining is to derive some valuable information or
the pattern which can be used in different sectors like marketing, education, healthcare industry.
Data mining are used in these sectors for different purpose as describe earlier. The ethical

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implication means to develop the mindset of stealing the data and carry out the business
accordingly. The well recognized problem with data mining is when the private data of the
individual is used to market the products in order to target. Though companies appear to focus on
the idea that more the data mining the more will be the sales of their products (Sharma &
Panigrahi, 2013). This might be acceptable with them but there will be disagreement with
customers.
Importance of these implications
The major issues related to the data mining are misuse of the customer data for the
purpose of marketing as discussed in ethical implication (Siemens & d Baker, 2012). The
customer has right to sue the company if he thinks his privacy is compromised. It is the duty of
the company to maintain the transparency in the usage of the big data and if there is any breach
in its data then it should take the responsibility for the loss.
Conclusion
From the assignment that the data mining has huge application in every sector of the
country is it finance, banking or a healthcare. The assignment also provides the news article
which shows how a company has utilized the concept data mining for the political benefits. The
demand for the data mining is not going to stop despite its misuses. The company’s duty to
maintain the transparency in the usage of the big data and if there is any breach in its data then it
should take the responsibility for the loss.

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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.

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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.