ITC 516 - Data Mining and Visualization for Business - Assignmnet

Running head: ITC516
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Name of Student
Name of University
Author’s Note

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TASK 1
Reasons for usage of data mining in the business
Why data mining is used?
Data mining is considered to be an important for the business process which helps to
study the pattern about the customer behavior towards its company. With the help of data mining
an unknown credible pattern can be explored which is helpful in business processing.
Various Sectors where data mining can be used
Data mining has wide application in various businesses some of them are listed:
CRM (Customer relationship management)
To establish a good relationship with the customer data mining can be used by the
company to analyze the customer data through which a certain pattern can be decoded (Wu,
2014). The decoded pattern can be used to retain, acquire customers and can also be used to
make strategies which is focuses on the customers.
In detecting fraud and lies
With the help of data mining a meaningful pattern is programmed and if any pattern
which is not valid is termed as a fraud, thus detecting it as a lie or fraud.
Education
In the education field the data mining is used for predicting the leaning behavior of the
student which is helpful for the institution to approximate the results of the student (Siemen & d
Baker, 2012).

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Financial Banking
In bank a huge amount of data is recorded every second who includes account number,
customer name, balance amount and many other things. In order to maintain such huge amount
of data and decode them to study the pattern of the customer way of doing banking, data mining
is used.
Healthcare business
Data mining helps to analyze data like best medicine practices, cost, volume of patients in
each category related data can be find out.
Recent article/news item relating to data mining business
Data mining start up enigma to expand commercial-business
The following article discusses about the death of five people due to the raging fire in the
New Orleans the fire was so intense that it engulfed the whole house the deputy mayor Andy
Kopplin condemned the news as the real tragedy but he also said that it as preventable if the each
houses in its neighborhood contained fire alarms. The officials decided to install fire alarms in
the houses which are at major risk of fire (Lohr, 2017). In order to select the house to install fire
alarms which is at most risk, they took the help of a startup company Enigma which is working
in the field of open data that involves collecting and mining public government information to
find the house which is at most risk.
The news article discusses that the officials in New Orleans are taking the help of the
technology in order to find the house which is at the most risk of the fire to install the fire alarm

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in them. The technology used here is data mining through the data mining enigma will analyze
the public records to gain insight to find the target houses.
Conclusion
From the article it can be concluded that data mining is not only limited for the business
purpose. If analyzed the importance of the data mining, it can be used in any field which is
shown in the above article that the data mining is used by New Orleans government to find the
house which is at most risk of fire to install the fire detectors (Miner, 2012). Big data is simply
the collection of huge amount of the data to derive a useful pattern which can be used as
information. Therefore it is applicable in the entire field which can be imagined.
References
Lohr, S. (2017). Data Mining Start-Up Enigma to Expand Commercial Business. Nytimes.com.
Retrieved 10 August 2017, from https://www.nytimes.com/2015/06/23/technology/data-
mining-start-up-enigma-to-expand-commercial-business.html
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.
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.
Miner, G. (2012). Practical text mining and statistical analysis for non-structured text data
applications. Academic Press.