Data Mining and Business: Applications, Implications, and Ethics

This report delves into the multifaceted world of data mining and its pivotal role in modern business operations. It begins with a foundational definition of data mining, highlighting its function in extracting valuable insights from vast datasets using database management, machine learning, and statistical methods. The report then explores the practical applications of data mining across various industries, including finance, health, retail, and production, showcasing its utility in market segmentation, customer churn prediction, fraud detection, and targeted marketing. A recent news article illustrates a real-world application of data mining, demonstrating its use by intelligence agencies for analyzing large datasets. The report critically examines the implications of data mining, including privacy, security, and ethical considerations, especially in light of the increasing data breaches and the potential misuse of personal information. It emphasizes the importance of transparency and ethical guidelines in data handling to maintain consumer trust and ensure responsible data practices. The report concludes by summarizing the benefits of data mining in business and stressing the need for a balanced approach that prioritizes both data-driven decision-making and the protection of individual rights.

Running head: DATA MINING AND BUSINESS
Data mining and business
Name of the Student
Name of the University
Author Note

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Executive summary
This report discusses about data mining. The main definitions and its usefulness in business
organizations or companies are shown here. The report further tells about the privacy, security
and ethical implications of data mining. This report is well addressed with many examples as
well.

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Table of Contents
Introduction:....................................................................................................................................3
Discussion:.......................................................................................................................................3
Task 1:.........................................................................................................................................3
Data mining in business:..........................................................................................................3
Recent news article about data mining:...................................................................................4
Task 2:.........................................................................................................................................5
Implications:............................................................................................................................5
Significance in business:..........................................................................................................7
Conclusion:......................................................................................................................................8
References:....................................................................................................................................10

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Introduction:
Data mining is a process where patterns in large data sets are discovered by using
methods of database management system, machine language and statistics. It is a sub section of
computer science where the intended goal is the extraction of information from a given data set
and transforming it to a simple language intended for future use. Data mining is the next step of
analysis of the discovery of knowledge in database process.
This report consists of the reason for requirements of data mining in business along with
a recent example to support the reason. This report also includes the security, ethical and privacy
problems of adopting data mining and some examples to support the statements.
Discussion:
Task 1:
Data mining in business:
Data mining is a new and very powerful tool with a potential to help companies or
business organizations aim towards the most important aspect about customer behavior and
behavior of new clients. Data mining is a type of discovery of knowledge, which is a computer-
operated process, which involves searching of large data sets and finding of patterns and
meaning in those data sets (Larose, 2014). The tools used in data mining processes is used to
predict the behaviors and trends in future which helps the company or business organizations
involved to make good decisions. Companies or business organizations in almost every
industries including finance, health, retail and production. By analyzing the data sets by pattern

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recognition technology and mathematical/statistical tools, data mining helps in identifying facts,
relations or patterns.
The detailed uses of data mining includes market segmentation which consists of
characteristics of the customers, customer churn which depicts the loyalty of the customer, fraud
detection which shows the likeliness of fraud transactions. It also includes direct marketing that
allows the highest conversion rates in the market, interactive marketing, which depicts the
interests of users accessing a website (Shmueli & Lichtendahl 2017). In addition, market basket
analyzing helps the companies or business organizations to understand the likeliness of products
to be purchased together and trend analysis, which shows the difference in behavior in customers
over a one-month gap.
Data mining in business involves technologies that help to provide historical, current and
predictable strategies that could be implied in the business. The common functions of data
mining in business operation include online analytics, reporting, process mining, event
processing, performance management, text mining and predictive analysis (Provost & Fawcett,
2013). Data mining can help the decision makers in a company or business organization to make
successful actions based on the information that is provided. It can also help the authorities to get
information on competitor marketing, condition of the market or behavior of the consumers.
Recent news article about data mining:
According to chicagotribune.com, a recent news article was published in 2013, which
showed collaboration between technical firms in Chicago with the venture capital of CIA ("CIA
venture arm invests in Chicago-based maker of artificial intelligence technology", 2017). Stuart
Frankel, the CEO of the startup claims to be very pleased with the portfolio and hopes that this

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will contribute to a market expansion of the firm. The name of the tech firm is Narrative Science
in Chicago.
For this collaboration, Narrative Science proposed the development of Quill, which is a
technology that will help the intelligence agency identify large data sets like video surveillances,
financial transaction and social media coverage. The intelligence algorithm of Quill will help to
analyze enormous amounts of data and provide data based in English. The program can produce
many types of formats, which ranges from tweets to business reports. The associated clients of
Narrative Science include many industries like marketing or financial services.
The news article also provides funny information about CIA Director David Petraeus
saying that the technology provided by them is much greater than the technologies seen in most
films (Goldman, 2015). In addition, they also have confirmed that the technology of removing
finger prints or eyeball images are not present but are supposed to be under development.
The link for this news article is -
http://articles.chicagotribune.com/2013-06-06/business/ct-biz-0606-narrative-science-
20130606_1_intelligence-community-in-q-tel-technology
Task 2:
Implications:
Data mining is used to get interesting patterns in data sets. The increasing implementation
of data mining in nearly every companies or organizations has created enthusiasm for
widespread adoption but on the same time, it has also increased risks and pressures of data
information acquisition. Data mining has shown great results in cases of biomedical research and
health. The early detection of epidemic outbreaks, detection of genomes or patterns of side

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effects by drug ingestion is the areas where data mining in big data has shown success ("Big
Data, Human Rights and the Ethics of Scientific Research – Opinion – ABC Religion &
Ethics (Australian Broadcasting Corporation)", 2017). However, the Snowden revelations, has
shown that how the use of big data and data mining led to database hack and other cyber-crime
which eventually led to undermining of trust, privacy, liberty and democracy.
Privacy has become too complex due to the evolvement of data mining. Originally, data
mining provided users to access information but with modernization, the impact has greatly
increased (Xu et al., 2014). This gathering of personal information has caused the rise of many
concerns in privacy. For example, data mining process can be used to get information of
individuals like number, address, social security id, driver’s license or e-mail. This has posed a
great concern for aggregation of personal information of these individuals and segregation of
these data to create user profiles, which can be used in both the government and commercial
sectors.
Security is another part where data mining is used. The information of individuals, which
can provide security implication, is analyzed beforehand. Data mining also checks individuals
with criminal activities to get insights and patterns of their work in criminal activities. It can also
check whether dangerous terrorists are involved with any particular patterns of crime. Since,
prediction of these activities are provided by data mining officials, careful requirements and
analysis of the details is required before taking necessary procedure against an individual
because it may happen that sometimes the data that is accrued might not have any link with the
undertaken investigation.

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Ethics is considered good if there is reason involved but also an obligation if pursuing of
information is required. For example, health care scientists who are engaged with big data are
not considered for their self-interest research in terms of money or fame but by the acquisition of
public materials, which is supposed to benefit people (Yoo et al., 2012). However, the use of
these goods depends on the ethics implied.
The right to privacy in the Universal Declaration of Human Rights is very unfamiliar
with high potential (Turner, 2014). The right tells people to imply their rights of acting in any
scientific inquiries and to benefit from them. The right to science allows favors participation of
citizens in scientific works. In modern times, the development in technologies caused an increase
in the number of citizens exercising their rights of participation in scientific works. In health
records, sometimes people demand their rights to check for information from newly made
surveys or equipments.
These rights to science and privacy are international rights but are also given force by
domestic or regional authorities.
Significance in business:
The collection of information by the use of data mining process has made the uses of it
legit as well as prone to abuses ("Big data security problems threaten consumers' privacy",
2017). For example, if the use of prediction can be applied to know the condition of the weather
at a future time, the more use of it will take place and this will invite security or privacy threats.
Due to the number of people affected in by breaches in the system, it is becoming a great
concern for data mining analysts. In 2014, the breach of Arkansas University system led affected

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50,000 people. In that same year, information of 145 million people was breached from eBay.
This has led to rethinking of implication of privacy and security to deal with the problems.
For buyers and consumers, the requirement of increased security in terms and conditions,
agreements and trust seals are required to be collected from the companies or organizations that
are involved in the collection of big data. The requirement for more security measures like
encryptions, detections of illegal access and corporate methods are being taken up in the
companies or organizations involved which will promote the security and tighten the relationship
with the consumers.
The requirements for increased revenue are an important goal that is present in every
business organization or companies. The need to deliver targeted advertising is achieved by
tracking the moves and preferences of the customers involved by the use of data mining and big
data. For example, the Personality Insights software of IBM helps to build a profile of an
individual, which is based on their online activities (Junior & Inkpen, 2017). These activities are
told as advantages to the customers who will help them to see valuable results but this is only
useful to the company or organization involved. For example, the insurance companies target
users based on these data personalities.
These concerns must be addressed as the power of data mining can be used to detect
fraudulent activities and can provide many advantages (Provost & Fawcett, 2013). The key to
achieve the power of data mining is transparency of these processes while providing security and
privacy concerns. The data handlers must provide valuable reasons of what data they are
collecting and analyzing and they reason behind that. People are also needed to be educated

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about the storage and collection of these data and companies must give satisfactory explanations
about the protection they provide to safeguard them which helps in building trust.
Conclusion:
Thus, it can be concluded from the report that there are vast uses of implementing data
mining in business. Its implementation helps the business organizations or companies to get
successful insights regarding customer behavior or market analysis. However, the risk it poses is
very much and it is the responsibility of the involved organization or business to gather the
necessary requirements to provide security in terms of privacy, security and ethics. This way the
company can improve their relationship with the customers and can stay in business for a long
period.

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References:
Big data security problems threaten consumers' privacy. (2017). The Conversation. Retrieved 9
August 2017, from http://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 9 August 2017,
from http://www.abc.net.au/religion/articles/2016/11/30/4584324.htm
CIA venture arm invests in Chicago-based maker of artificial intelligence technology.
(2017). tribunedigital-chicagotribune. Retrieved 9 August 2017, from
http://articles.chicagotribune.com/2013-06-06/business/ct-biz-0606-narrative-science-
20130606_1_intelligence-community-in-q-tel-technology
Goldman, J. (Ed.). (2015). The Central Intelligence Agency: An Encyclopedia of Covert Ops,
Intelligence Gathering, and Spies [2 volumes]: An Encyclopedia of Covert Ops, Intelligence
Gathering, and Spies. ABC-CLIO.
Junior, R. A. P., & Inkpen, D. (2017, May). Using Cognitive Computing to Get Insights on
Personality Traits from Twitter Messages. In Canadian Conference on Artificial Intelligence (pp.
278-283). Springer, Cham.
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley
& Sons.

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Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data
mining and data-analytic thinking. " O'Reilly Media, Inc.".
Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data
mining and data-analytic thinking. " O'Reilly Media, Inc.".
Shmueli, G., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts,
Techniques, and Applications in R. John Wiley & Sons.
Turner, B. (2014). Universal Declaration of Human Rights. The Statesman’s Yearbook: The
Politics, Cultures and Economies of the World 2015, 8-10.
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information security in big data: privacy
and data mining. IEEE Access, 2, 1149-1176.
Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L.
(2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical
systems, 36(4), 2431-2448.