1.Briefly Summarise why Data Mining is used in Business.
2.Share a recent article/news item relating to Data Mining in Business.
Importance of data mining
Data mining is an important for a business process as it helps to gain insight about its customer behavior towards its company. Data mining helps to explore unknown credible pattern which is significant for business success.
Usage of data mining in the business
Data mining is used in various sectors for different purpose some of them are:
Education
The Educational Data Mining which is concerned with developing various methods which will predict students’ future learning behavior which can be used by an institution to take accurate decisions in order to predict the results of the student.
Customer Relationship management
In order to maintain a good relationship with a customer the company examines their customer data which helps to decode a certain pattern .These pattern will help them to acquire, retain customers and to implement customer focused strategies.
Financial Banking
(Sharma & Panigrahi, 2013) In order to maintain these data in a logical manner data mining is used. The bank also uses customer banking pattern in order to make various schemes and strategy.
Fraud and Lie detection
Huge amount of money have been lost by the companies in the action of frauds. (Uzar, 2014). 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.
Healthcare
( Libert,2015)Data mining helps in analyzing data like best medicine practices, cost, volume of patients in each category related data can be find out.
Benefits of using Data mining
There are various benefits of data mining in various sectors of the business
Recent article/news item relating to data mining in business
In Australia’s national tourism annual talkfest, the managing director of the Google Australia Jason Pellegrino addressed the people on the agenda that data mining is the future of tourism Australia.
He said that in the future the artificial intelligence has bigger role in determining the customers need and deliver the required service to them (Data mining the future of tourism, 2017). He said that proper utilization of “cookie crumbs”, a website stored in the computer which is of user preferences. The main of motto of using cookie crumbs is to anticipate someone’s needs before he knows what he wants for himself. Google is training around 12000 engineers to use these cookie crumbs in data mining in order to analyze what the customer needs. When people are searching for a specific destination, their information is stored with them and if they’re open to sharing those data with Google it can deliver the personalized service to the person.
Conclusion
Increasing competition in the field of “services in tourism” has enabled the companies to provide more efficient and fast services to tourist. Google has identified such way by using cookie crumbs and it took the help of data mining to study the tourist needs and preferences and providing them with the same when required. The data mining has helped to forecast the expenses of the tourism, using cookie crumbs to analyze the tourist preferences like the accommodation range, preferred food, activities, shopping, events and target the particular group with the same choice.
This report discusses about the concept data mining and the security issues in data mining, privacy issues in data mining, ethical implications in data mining. It further discusses about the importance of these implications in business and concludes with what steps company takes in order to use data mining and also protect individual privacy.
Major Security Issues in Data mining
As more personal information are being entered in computer it is to access any persons data but at the same time it has also been misused (Begum, 2013). Big data has huge application to change the definition of technology like predicting the result before two to three days of its occurrence but it also face huge challenge to protect these data from malicious use which can be privacy threat to a person. Following are some of the threat related to big data:
The size of the data
With huge amount of the data present in the big data it is the big challenge for the company to protect the customer’s privacy. If there is single breach in the data thousands of customer’s data is leaked thus compromising the privacy ( Che &Safran & Peng, 2013). In 2014 the breaching in Arkansas University fifty thousand peoples were affected and same year the data breach in eBay has compromised over 145 million people data. Amazon relies on distributed computing where data is distributed to its twelve places where it has its operation which can minimize the effect.
The access control difficulty
In order to control the access of information it is recommended to have single access point, but in case of big data where the data is widely placed and have various access point thus increasing the vulnerability of security (ElAtia &Ipperciel& Hammad, 2012). Various software’s does not take the security as its priority, further increase the risk. For instance Hardtop, a 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
When the customer demands more security for their data, encryption, access control, intrusion detection, backups, auditing are some of the procedure company will follow but in the name of high security the company demands more private information of the customer (Dev et al, 2012). To improve the security the agencies treats every customer as potential criminal who can steal the data, even if the agency has enough information to prove that the certain customer is not the terrorist it still makes more decrypted version of his data.
Big data usage
Apart from the privacy concerns there are various other ways big data is providing the customers private data. In order to target the customer with his desired advertisement big data is helping various companies to track the online move of the customer and analyze the customer’s choice (Mali &Ghazi & Ali, 2012). The company can claim that they are studying the customers move in order to make online experience friendlier but the same information can be used against the customer.
The digitization is radically transforming human lives. To increase the capability to amass and store data and analyze them for yielding knowledge. On one hand the big data has the potential applications in various sectors but on other hand the Snowden revelations about government surveillance has created doubt about the usage of Big Data which can compromise not just privacy, but will also fail the trust among people . The rising fears about hacked databases, breaching of data and other cyber-crime have created fears which can undermine the application of the digital world. EHR is one of the standard practices which can change the face of health care. The use of EHRs is to record the patient information that is utilized in clinical care of the patient, despite of its capability EHR data are not in practice in health research and the main reasons for underutilization is worries about privacy. Therefore some conditions should be applied in order to use the big data
The rise of the Big Data is an example of how it application have generated huge benefits and high risks. Therefore to overcome the challenge to use benefits with minimal risk, company have to use the ethical thinking to find the possible solution.
The central idea of data mining is to analyze the large and complicated databases and derive some information or pattern which is beneficial for the company .Data mining is successfully used in healthcare, marketing, education and other business organizations. Data mining are used in these sectors to examine the customer behavior to envisage the trends which can help in enhancing a company’s revenue or profits. Ethical implications on businesses utilizing the data mining are much different from legal implication (Stormier & Piazza, 2013). Stealing or hacking a data is illegal and comes under legal implications, but developing a mindset to steal the data is unethical. So, the real problem which concerns people is , when the companies tries to utilize the information which violates their privacy in order to use the information which can aim back with more products, they consider it unethical. But despite all of this, ethical issues related to data mining are neutral ground. The entire concept of data mining cannot be considered as illegal as it has huge application. The data mining technology is not going to stop despite its limitation on the contrary it is going to expand as more companies are using digital applications. The most recognized issue with data mining is when the private data of the individual is used to market the products in order to target others. The companies seem to imply the idea that more the data mining the more will be the sales of products. This might be true but there will be disagreement with customers.
The implication is important in order to protect customer’s private information. If anyone tries to use the information then the customer has full right to sue as it’s his rights to protect his personal rights (Willis ,2013). No company has right to use someone information without his consent for marketing.
Conclusion
From the report it can be concluded that despite data mining has revolutionized the way of business in various sectors like healthcare, education, finance and marketing, it helped the company to reach more customers by studying their patterns of shopping. It is also capable of detecting frauds and thefts but despite all the advantages various companies misuses the technology by using the peoples personal information without their consent for marketing therefore the company has to take responsibility that they will protect the customers privacy and be accountable if there is any such breaches.
References
Data mining the future of tourism. (2017). Couriermail.com.au. Retrieved 3 August 2017, from https://www.couriermail.com.au/technology/google-australia-tells-tourism-australia-mining-internet-users-data-is-industrys-future/news-story/23377e9d8932615c7638e238079e8b75
Libert, T. (2015). Privacy implications of health information seeking on the web. Communications of the ACM, 58(3), 68-77.
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.
Begum, S. H. (2013). Data Mining Tools and Trends–An Overview. International Journal of Emerging Research in Management &Technology, ISSN, 2278-9359.
Che, D., Safran, M., & Peng, Z. (2013, April). From big data to big data mining: challenges, issues, and opportunities. In International Conference on Database Systems for Advanced Applications (pp. 1-15). Springer, Berlin, Heidelberg.
Dev, H., Sen, T., Basak, M., & Ali, M. E. (2012, November). An approach to protect the privacy of cloud data from data mining based attacks. In High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion: (pp. 1106-1115). IEEE.
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.
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.
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.
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