A Discussion on the Acceptability of Data Mining Essay

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Data mining has been defined differentially in diverse contexts, but the major underlying theme is that it is an activity that engages in nontrivial extraction of previously unknown data for purposes of collecting useful information that can be applied to a wide-range of settings.

Today, more than ever before, individuals, organizations and governments have access to seemingly endless amounts of data that has been stored electronically on the World Wide Web and the Internet, and thus it makes much sense for these entities to internalize the desire to analyze and synthesize this data in a focused attempt to discover meaningful patterns hidden within the data (Ethics in Computing para. 1-2).

However, it should always be remembered that data mining may occasion devastating effects if proper regulations are not adopted by the participating entities or stakeholders. The purpose of this paper, therefore, is to demonstrate that data mining is acceptable and advantageous if proper regulations are put in place.

The first reason why data mining is acceptable is that it facilitates organizations to provide better services to customers. This capability provides organizations with a distinct advantage over competitors as they are more able to learn about customer purchase behaviors, beliefs and expectations through data mining. Indeed, extant literature demonstrates that data mining assists organizations “to build detailed customer profiles, and gain marketing intelligence (van Wel & Royakkers 129).

Within the business context, therefore, it can be argued that data mining assists marketers and business organizations to not only build models based on data to predict the target audience that is likely to respond to new marketing initiatives or new products in the market, but also to reinforce customer buying behavior and experience (Zentut para. 2).

However, companies utilizing data mining to forecast customer trends should understand that there is always a probability of breach to security, which may lead to several negative ramifications, including theft of sensitive personally identifiable information. Such risks should be countered by putting in place adequate security measures to guarantee information privacy (van Well & Royakkers 138).

The second reason why data mining is acceptable is that it assists governments to not only provide effective and efficient services to citizens, but also identify and deal with criminal activities.

Indeed, extant literature demonstrates that “data mining helps government agency by digging and analyzing records of financial transaction to build patterns that can detect money laundering or criminal activities” (van Well & Royakkers 132). This is a worthy function considering the fact that governments all over the world lose huge amounts of money annually to money launderers.

Of course there exists a threat arising from the fact that government agencies may fail to exercise ethical responsibilities of data disclosure when dealing with personally identifiable information, but such a threat can be addressed through developing and implementing stringent security measures and rules of engagement when dealing with sensitive personal data. Privacy and confidentiality of data must be maintained at all times for data mining to achieve its desired objective within this context (Seltzer 1442).

The third reason is premised on the fact that data mining can be applied within the manufacturing sector to “detect faulty equipments and determine optimal control parameters” (Zentut para. 5).

This is a noble achievement by virtue of its capability to ensure the products coming out of our factories are safe to use and can also be depended upon to make life easier and more fulfilling. This benefit is intrinsically tied with a disadvantage that industries may engage in misuse of information or the usage of inaccurate information (Zentut para. 9), but then statutory and governmental rules and regulations governing the use of data mining information should be put in place to avoid misuse.

Works Cited

. n.d. Web.

Seltzer, William. . n.d. PDF file. Web.

Van Wel, L and Lamer Royakkers. “Ethical Issues in Web Data Mining.” Ethics and Information Technology. 6.1 (2004): 129-140. Web.

Zentut. . 2013. Web.

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IvyPanda. 2018. "A Discussion on the Acceptability of Data Mining." December 19, 2018. https://ivypanda.com/essays/a-discussion-on-the-acceptability-of-data-mining/.

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IvyPanda. "A Discussion on the Acceptability of Data Mining." December 19, 2018. https://ivypanda.com/essays/a-discussion-on-the-acceptability-of-data-mining/.

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