The Role Of Data Mining In Business And The Issues Of Security, Privacy And Ethics

Data Mining in Business

1.Briefly Summarize why Data Mining is Used in Business.

2.Identify the Major Security, Privacy and Ethical Implications in Data Mining.

In this new world of technology digitization and big data are showing their potential to revolutionize the lives of this new era. Data mining is changing the face of the business and helping in the new and more effective way of business style which will be better than traditional approaches.  Data mining is the extraction of useful relationships and patterns from sources data like, databases, web and the texts. This report emphasis on the role of data mining in the business and it’s affect on the lives of people who are accessing internet or comes under data mining.

In a wide range of industries many companies nowadays including, finance, retail, health care, 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. It can be used in developing smarter marketing campaigns, spotting sales trends, and accurately prediction about the loyalty of a customer (Gupta & Aggarwal, 2012). Application of data mining can be listed as: firstly, Market Segmentation: Identifying common characteristics of the customers buying same product from same organization. Secondly, Customer Churn: Prediction about the employee who can leave the company and move to the competitor. Thirdly, Fraud Detection: detection of the fraudulent transactions. Direct Marketing: In order to obtain highest response rate of the customer on a product, it identifies which prospects should be included in the mailing list. Interactive Marketing: Prediction which website is more interested to access by the customers. Market Basket Analysis: Helps in understanding which products are commonly purchased together. Trend Analysis: Revealing the difference between the typical customer of current month and last month (Farooqi & Raza, 2012). It also helps in generating new business opportunities by automated prediction of behaviors and trends and by automated discovery of previously unknown patterns.

Article Name: A review on knowledge extraction for Business operations using data mining

Link: https://ieeexplore.ieee.org/document/7943205/

Bharara, Sabitha and Bansal (2017) discussed the integration of Data Mining with principles of practices of Knowledge Management by presenting a table of research areas in Knowledge management by different authors. The research was made to explain how data mining can be used for Knowledge Management in Operations management. They stated that “Data Mining as an area is almost contemporary to business”. Various techniques like association, classification, clustering and text mining can be used to extract or deliver or support knowledge in key operational activities. This article emphases on the association of mining in business can be used for quality management and ethics related to operations. There are two cases proposed in this article on how classification technique of Data Mining can be used for the classification of Knowledge Operations which are: Firstly, New explicit Knowledge operations are classified on the components of operation which are related to an individually. Secondly, Existed explicit Knowledge Operations can be classified under an appropriate component of operations. Based on the article it can be concluded that in the prospective of business knowledge provides guidance and help an individual to formulate decision and apply them to work for better performance (Zorrilla & Gracia-Saiz, 2013). Various models of applications and review of Data Mining on those applications are proposed in this article for their application in business. Other data mining techniques like Outlier Analysis, Time Series can also be applied in the business is also proposed in this article. Snowden revelations about surveillance of government over civilians have highlighted the growing level of fears of misuse of the big data as explained in the second article.

Significance of Data Mining Implication in Business

Major Security, Privacy and Ethical Implications in Data Mining

 Nowadays more personal information and data are being collected in the memory of computers in the form of Big Data which can be beneficial as well as abuse if it is used maliciously (Frank, Hall & Pal, 2016). This can result in the threats to the privacy and security threats for an individual or any company. Based on the discussion in the article ‘Big data security problems threaten consumers’ privacy’ potential of problems may be very large as they may affect the security and privacy of a very large proportion. This may be explained based on the evidence provided in the article that about 145 million people were affected when there was the data breach at eBay in 2014 (Garrie & Mann, 2014). This breach lead to the expose of residential addresses, birth dates, email address and other information which can be considered as a big concern in implementing data mining in business. Using banking transaction details and pharmaceutical records for data mining seems more intrusive for the privacy of an individual than tastes and lifestyle data. A set of guidelines (OCED 1980) is produced by the Organization of Economic Cooperation and development (OCED) in 1980 for the protection of personal data of an individual. Data mining potentially violates the principles of OECD which are firstly, reasons should be made clear about storing the personal data of an individual and secondly, the data cannot be used for any purpose other than stated. Discussed by Zeide (2015) in an article that it is generally de-contextualized and separated from the individual when personal data is being collected in order to improve privacy but misusing it more likely. It can be seen that it is very inappropriate in terms of human rights to trade in personal data and information of an individual as discussed in the article ‘Big Data, Human Rights and the Ethics of Scientific Research’. In the prospective of ethical threats there may be chances of making mistakes who are practicing data mining and which lead to consequences of losing personal information. Data mining can be classified correctly in some of the cases but these classifications could be on ethical sensitivity controversial attributes like race, sex, sexual orientation or religion (Provost & Fawcett, 2013). It may be hard to identify the use of controversial classification attributes. Possible solutions according to the first article may be encryption, intrusion detection, backups, access control, corporate procedures and auditing which can protect data from getting breached by unauthorized individual or falling into wrong hands.

Major Security, Privacy and Ethical Implications in Data Mining

Significance of Data Mining Implication in Business

Major elements in data mining are firstly, extraction, transformation, and load transactional data onto the data warehouse system. Secondly, Storing and managing the data in a multidimensional database. Providing access to saved data for professionals of Information Technology and business analysts. Fourth, analyzing the data by application software and fifth, presenting the data in a useful format like graph or table. Functioning of data mining and the variety of knowledge discovered by data mining can be helpful in understanding the significance of data mining in business sector which are: First, Characterization: It can be defined as the production of characteristic rules which is produced by summarizing general features of the object in a target class. Second, Discrimination: It is simply comparison between the features of the objects of target class and constraint class. Third, Association analysis: Determination of frequency of the objects bought together in the transactional databases which are based on threshold called support which identifies the sets of frequent items. Fourth, Classification: Classification is an algorithm learned from the training set and builds a model based on this set which is used to classify new objects (Jacob & Ramani, 2012). Prediction: A successful forecasting in the context of the business one can be unavailable values of data or pending trends or predict a class label for few data. Clustering: It is similar to classification up to some extent but it is organization of a data in classes, unlikely classification its classes are unknown and discover acceptable classes. Outlier analysis: Generally exceptions or surprises data which cannot be grouped in a cluster or in any class. Evaluation and deviation analysis: It pertains to study of time related data those changes in time which are evolutionary trends in data. Applications of data mining can also be helpful in understanding the significance of data mining in business sector which can be listed as: First, application of data mining in healthcare, second data mining tools can help in future direction of the health care system. Third, application of data mining is in many areas of manufacturing engineering. It is also applicable as an emerging trend in educational system which can be domain specific or generic.

Conclusion

Based on the above report it can be concluded that data mining is very useful in the aspect of business but it has certain consequences which are very necessary to be eliminated or minimized to extent level. Data mining will help in improving the way of business by an organization or company but it may affect the privacy, security and ethical values of an individual as explained in the above 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.

References:

Bharara, S., Sabitha, A. S., & Bansal, A. (2017, January). A review on knowledge extraction for Business operations using data mining. In Cloud Computing, Data Science & Engineering-Confluence, 2017 7th International Conference on (pp. 512-518). IEEE.

Farooqi, M., & Raza, K. (2012). A Comprehensive Study of CRM through data mining Techniques. arXiv preprint arXiv:1205.1126.

Garrie, D., & Mann, M. (2014). Cyber-Security Insurance: Navigating the Landscape of a Growing Field. J. Marshall J. Computer & Info. L., 31, 379-657.

Gupta, G., & Aggarwal, H. (2012). Improving customer relationship management using data mining. International Journal of Machine Learning and Computing, 2(6), 874.

Jacob, S. G., & Ramani, R. G. (2012). Data mining in clinical data sets: a review. IJAIS-ISSN: 2249-0868 Foundation of Computer Science FCS, New York, USA, 4(6).

Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. ” O’Reilly Media, Inc.”.

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].

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].

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

Zeide, E. (2015). Student Privacy Principles for the Age of Big Data: Moving Beyond FERPA and FIPPs. Drexel L. Rev., 8, 339.

Zorrilla, M., & García-Saiz, D. (2013). A service oriented architecture to provide data mining services for non-expert data miners. Decision Support Systems, 55(1), 399-411.