1.Briefly summarise why data Mining is used in Business.
2.Discuss about the Security, Privacy and Ethics.
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.
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.
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.
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.
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.
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 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.
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
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 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.
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). IEE
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