Data mining Advantages
Introduction
Data mining is a new issue that emerged with the use of computers and digital technology. According to Oatley and Ewart (2011), data mining entails a series of activities for finding trends, glitches, and correlations within vast data sets to forecast outcomes. Data mining is explored for business intelligence purposes, marketing, and other reason. Various techniques are deployed to mine data from online sources (Li et al., 2020). Information gathered can be used to reach out to targeted customers, enhance revenues, reduce costs, cut risks, and better customer relations.
I support data mining because of the numerous benefits to the general society and its contribution to development. I do this despite concerns about ethical practices in data mining, which have elicited much opposition, especially from the human rights activists who feel that data mining breaches users’ privacy.
The purpose of this research is to come up with compelling information on why data mining is still a good practice, regardless of the concerns raised about a breach of privacy and unethical use of private information. In a nutshell, at the end of the study, I intend to have firmed and defended my pro-data mining position, highlighting some of the key advantages of the exercise, meriting its continuation.
Whereas some people think that data mining is unethical and thus should be outlawed, but the practice has more advantages than disadvantages, hence the need to permit data mining.
Section Two: Counterargument
The anticipated opposition to data mining includes concerns about the breach of user privacy. Data mining includes gathering information from online users, sometimes with their prior consent or without consent. The data captured through mining may include information on sites an individual visits often, when he or she lives, friends and relatives, and personal details like addresses and contacts (Oatley & Ewart, 2011). When a person shares some of this information online, he or she does not expect that it can be taken by someone else for other purposes. Sites that a person visits privately and would not want information about it shared can be tracked, noting what an individual likes searching while online. It is on these grounds that the opponents of data mining cite a possible breach of privacy. Therefore, the opponents of data mining would want to paint it as a purely dangerous activity that should be flatly rejected, ostensibly without looking at the merits side and how it can be regulated to reduce the negative aspects.
Response to the opponents to data mining
Data mining is not always unethical; individuals and organizations can conduct ethical data mining to help collect reliable information that can support decision-making. Data mining is one of the most efficient ways of gathering information than the traditional survey. Businesses have been able to leverage data mining to reduce the cost of production, enhance the consumer experience, and reduce the prices of products and services offered (Shmueli et al., 2007; Loebbecke, 2019).
Section 3: Audience
The targeted audience is diverse, bringing together the young and the old, lawmakers and business people, computer and IT experts, civil rights activists, and the general public. Data mining affects all people who access and use internet services, hence the diversity of the audience. The lawmakers are critical to formulating rules and regulations that govern how individuals and organizations can use computer and internet services.
The goal
The goal for the audience is to convince them that data mining is good for our society (Brown, 2014). Mining data can be ethical, allowing the use of gathered data for the general benefit of society; it does not always have to be unethical.
Section Four: Strategy
The strategy to convince my audience would include emphasizing the benefits of data mining while at the same time explaining how it is possible to put in place control measures to eliminate unethical practices in mining and the use of data. To attain this, I would stress the ethics, ethos of data mining, and logos, the logical use of mined data to better human life (Shmueli et al., 2007). I would also appeal to the emotions about the digitalization of the world and how society can leverage it to improve its welfare.
Section Five: Research approach
The research study will take a qualitative approach to explore issues surrounding data mining. A systematic review of existing literature shall be conducted to support the main goals of the study. The sample will include about ten articles and books, all relevant to the topic of study. The resources used will be sampled based on their importance and applicability to the research study.
References
Brown, M. S. (2014). Data mining for dummies. For Dummies, a Wiley Brand.
Li, M., Wang, H., & Li, J. (2020). Mining conditional functional dependency rules on big data. Big Data Mining And Analytics, 3(1), 68-84. https://doi.org/10.26599/bdma.2019.9020019.
Loebbecke, C. (2019). Digitization, Big Data Analytics, and Artificial Intelligence Transforming Business, Society, and Research. Moscow University Economics Bulletin, 2019(6), 9-11. https://doi.org/10.38050/01300105201962
Oatley, G., & Ewart, B. (2011). Data mining and crime analysis. Wiley Interdisciplinary Reviews: Data Mining And Knowledge Discovery, 1(2), 147-153. https://doi.org/10.1002/widm.6.
Shmueli, G., Patel, N. R., & Bruce, P. C. (2007). Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. John Wiley & Sons.
