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.
There are various applications of the data mining in companies in the market in different field. Some of the applications are discussed below:
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.
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 the data with respective customer. The data mining helps in maintaining a huge pool of data and allocate with appropriate customer.
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.
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.
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 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.
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.
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.
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 customer data and information the data and information of the customer can be hacked by the intruders.
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.
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.
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 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.
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.
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. The ethical consideration of the customers helps in maintaining the proper brand image of the company in the market.
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.
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 https://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.
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.
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