Data Mining can be defined as transforming data into actions. Data Mining starts with the identification of right opportunities for the business by identifying relevant data and brings them together as a factor of success of the business.
Importance of Data Mining
In a wide range of industries many companies nowadays including, finance, retail, healthcare, aerospace, and manufacturing transportation are using Data Mining tools and techniques to take advantage of historical data about an individual or any information related to him or any organization. Data Mining can be used in better decision taking in business by discovering patterns and relationships in the data (Anandrajan, Anandrajan & Srinivasan, 2012). It helps in developing smarter marketing campaigns, spotting sales trends, and accurately prediction about the loyalty of a customer.
Application of Data Mining in Business
- In order to obtain highest response rate of the customer on a product, it identifies which prospects should be included in the mailing list known as Direct Marketing.
- Data Mining help in ‘Market Basket Analysis Helps’, which means detection of the products bought together by a customer (Hoffmann & Klinkenberg, 2013).
- Data Mining can detect the fraudulent transactions.
- Trend Analysis is another application of Data Mining as it reveals the difference between typical customer of current month and last month (Farooqi&Raza, 2012).
- Predict about the employee who can leave the company and move to the competitor (Customer Churn).
- Identify common characteristics of the customers buying same product from same organization, defined as market segmentation.
- Prediction which website is more interested to access by the customers.
Benefits of Data Mining in Business
- Increases company revenue
- Helps in decision making
- It depends on the analysis of market (Shmueli & Lichtendahl, 2017)
- Signifies customer’s behavior
This article emphases on the solution to the most important question in the modern finance which is ‘how to find an efficient way to visualize and summarize the stock market data in order to provide an organization or institution the information about the behavior of the market for investment decisions?’.
The solution provided for the problem in this article is Data Mining (Radhakrishnan, Shineraj & Anver, 2013). This article states that researches on Data Mining are increasing due to the importance of the application of Data Mining and big storage of information and data. Researches made in this article provide an overview of the application of Data Mining techniques for the improvement of business. A decision tree has also been proposed in this article in order to support Data Mining role is beneficial for marketing and business. Problems of economic, business interest and intellectual have been proposed in this article in the form of six tasks which are classification, estimation, prediction, affinity grouping clustering, and description and profiling. Identification of opportunities in business have also been proposed in this article including the normal business processes those are good for Data Mining as; planning for introducing new product to the market, planning for direct marketing campaigns, followed by understanding customer churn/attrition and evaluating results.
Conclusion on article review
Based on the decision tree proposed in the article, conclusion made by writers is that there has been a critical need of automated approach for the effective and efficient use of financial data to support organizations in planning and making good investment decision including prediction of the market by the implication of Data Mining.
Based on the above information it can be concluded that Data Mining can be one of the efficient and effective factor for the success of a business. Data Mining patterns can be used to uncover hidden patterns and allow businesspersons or an organization to make decisions based on the data collected and bring the business to the top.
This report aims on the application of Data Mining in businesses, which have beneficial aspects in enhancing the business, but it may lead to several security, privacy and ethical issues for an individual. Information can be paraphrased as power and greater responsibilities come with greater power. As per the definition, Data Mining collects various personal information of an individual for good purpose but this may lead threats to the privacy and security of an individual (Xu et al., 2014). This data may have collected for good purpose but with unauthorized access of any individual may expose the whole privacy
Major Security Issues in Data Mining
Application of Data Mining in business may result in the threats to the security of an individual. Personal information and data are being collected in the form of Big Data in the databases of computer which can be beneficial as well as abusive by using this information maliciously (Wu et al., 2014).‘Big data security problems threaten consumers’ privacy’ discussed that potential of security issues may be in a large proportion. Considering the breach that happened in the JP Morgan Chase & Co. in which approximately 83 million people were affected as their bank account details and residential addresses were hacked which may lead them towards becoming prey of any ransom ware accident (Silver-Greenberg, Goldstein & Perloth, 2014).
Privacy Issues in Data Mining
Based on the evidence provided in the first article that 145 million people were affected when there was a data breach at eBay in 2014 (Garrie & Mann, 2014). In this breach, there wasexposure of email address,residential addresses, date of birth, and other information to the whole world. This is a serious concern in aspect of privacy of those users. Using banking transaction details and pharmaceutical records for Data Mining seems more intrusive for the privacy of an individual than tastes and lifestyle data of an individual. After reviewing second article it can be seen that in 1980, a set of guidelines (OCED 1980) have been proposed by the Organization of Economic Cooperation and development (OCED) for the protection of personal information of an individual. It can also be stated that Data Mining may potentially violates the basic principles of OECD which are listed in the second article as: firstly, proper reason should provide by the organization for collecting and saving personal information and secondly, that data should not be used for any other purpose except stated reasons for the collection of data.
Ethical Implication of Data Mining in Business
It is generally de-contextualized and become separated from an individual when personal information or data is being collected to improve privacy but this can be misused in many ways. Based on the discussion in the article ‘Big Data, Human Rights and the Ethics of Scientific Research’ it can be conceptualized that it is inappropriate to an extent level to trade personal data or information of an individual viewing from the side of human rights (Tasioulas, 2017). With respect to the ethical threats, there are chances of making mistakes by them, who are practicing Data Mining for the benefit in business or for the organization and this can lead to serious consequences of losing personal information of several individuals at once. In some cases Data Mining can be classified properly but these classifications could be on controversial of ethical sensitivity attributes like race, sex, sexual orientation or religion (Custers & Schermer, 2014). Identifying the use of controversial classified attributes seems very hard in practice. Possible solutions according to the first article may be intrusion detection, access control, encryption, backups, auditing, and corporate procedures by which an individual or an organization can protect its informational assets and data from being breached by unauthorized user access.
Importance of Ethical implications in Data Mining
Ethical issues arise due to the implication Data Mining in business, organization and government. These issues cannot be considered, as a beneficial aspect for any individual where as provides many advantages to the organization or government but the cost of this is the privacy of the citizens. These implications will help those individuals to protect their privacy from being exposed to the world (Crane & Matten, 2016). Here privacy refers to the personal information not personal likes, which can ultimately help in increasing security from them. These implications will provide individuals rights to keep their privacy and be secured in day-to-day work. Organizations will become forced to keep the collected information and data more secure and invest more on protecting that personal information collected during Data Mining by the ethical implications in Data Mining.
Based on the above report it can be concluded that it is need of the time to eliminate or minimize all the threats that arises due to the implication of Data Mining in business. Nowadays it is being estimated that every new organization is implementing Data Mining for the benefits of their business and government is using this technology to keep cities or countries safe but this may lead to various issues, which are explained above in the report. The above report discusses on the role of Data Mining in the business and their consequences and an article review is presented to support the reported information. Significance of Data Mining is also proposed in this report based on the two articles provided and supporting it with recent references. It can also be concluded that Data Mining methods, techniques and tools are useful in a variety of areas with different applications.
Anandarajan, M., Anandarajan, A., & Srinivasan, C. A. (Eds.). (2012). Business intelligence techniques: a perspective from accounting and finance. Springer Science & Business Media.
Crane, A., & Matten, D. (2016). Business ethics: Managing corporate citizenship and sustainability in the age of globalization. Oxford University Press.
Custers, B. H., & Schermer, B. W. (2014). Responsibly Innovating Data Mining and Profiling Tools: A New Approach to Discrimination Sensitive and Privacy Sensitive Attributes. In Responsible Innovation 1 (pp. 335-350). Springer Netherlands.
Garrie, D., & Mann, M. (2014). Cyber-Security Insurance: Navigating the Landscape of a Growing Field. J. Marshall J. Computer & Info. L., 31, 379-657.
Hofmann, M., & Klinkenberg, R. (Eds.). (2013). RapidMiner: Data mining use cases and business analytics applications. CRC Press.
Radhakrishnan, B., Shineraj, G., & Anver Muhammed, K. M. (2013). Application of Data Mining In Marketing. IJCSN International Journal of Computer Science and Network, ISSN (Online), 2277-5420.
Ryoo, J. (2017). Big data security problems threaten consumers' privacy. [online] The Conversation. Available at: https://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798 [Accessed 6 Aug. 2017].
Shmueli, G., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
Silver-Greenberg, J., Goldstein, M., & Perlroth, N. (2014). JPMorgan chase hack affects 76 million households. New York Times, 2.
Tasioulas, J. (2017). Big Data, Human Rights and the Ethics of Scientific Research – Opinion – ABC Religion & Ethics (Australian Broadcasting Corporation). [online] Abc.net.au. Available at: https://www.abc.net.au/religion/articles/2016/11/30/4584324.htm [Accessed 6 Aug. 2017].
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.
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.