Data Mining
Data Mining, A Major Asset for Companies & Computer Science
- Introduction
Opening sentence or hook:
Data mining is a topical issue that has gained prominence in today’s world because of technology.
Explanatory sentence about topic/problem:
The topic has two sides, pro and anti-data mining. The anti-data mining group argues that mining data from online users breach their privacy and exposes them to security risk (Shmueli et al., 2020, p. 15). On the other hand, pro-data mining argues for the ethical use of mined data. I think data mining should be left to be but with regulations to address the issue of unethical harvesting and use of users’ data. Data mining has helped the business organization reach out to their clients and improve their products and services to meet consumer taste and preference. It is also used in marketing and solving security issues. My audience would be lawmakers, computer experts, and users, and the general public (Shmueli et al., 2020, pp. 78-79). The objection to my proposal would be that data mining breaches privacy, and the exercise of mining data cannot be fully monitored to ensure that there are no unethical practices.
Thesis statement:
Whereas some people think that data mining is unethical and thus should be outlawed, banning the practice has more advantages than disadvantages, hence the need to permit data mining.
- Supporting Claim 1
Topic Sentence:
Data Mining is ethically used by companies in marketing and strategic planning (Ahlemeyer-Stubbe & Coleman 2014, p. 14). Ahlemeyer-Stubbe and Coleman shows how companies have turned to technology to enhance gathering business intelligence and application. The text demonstrate how data mining is a big asset for business firms because it helps them to gain broader and accurate business intelligence than it was when using traditional tools (Ahlemeyer-Stubbe & Coleman 2014, p. 17).
Transitional Phrase:
Business intelligence is no longer reliant on humans to collect and analyze primary data.
- Supporting Claim 2
Topic Sentence:
Studies have demonstrated that unethical practice in data mining can be managed through rules and regulations to protect the sector (Brown, 2014, p. 18). According to Brown (2014), like any other lawful practice, data mining is not immune from unethical practices that can be addressed through the law and other measures. The book shares information on how unethical practices in data mining can be managed to ensure that its benefits are not overshadowed (Brown, 2014, p. 54).
Transitional Phrase:
The victims of ethical breaches in data mining think that all that happens in the industry has to be immoral and to the detriment of users.
- Supporting Claim 3
Topic Sentence:
Data mining has helped business firms to reduce the cost of production and improve customer experiences (Loebbecke, 2019, p. 9). According to the article “Digitization, Big Data Analytics, and Artificial Intelligence Transforming Business, Society, and Research,” technology has enabled companies to leverage computer and digital technologies to access a huge set of data about clients and competitors, from which they can discover trends impacting their activities and plan how to improve products and services to clients (Loebbecke, 2019, p. 10).
- Conclusion
Banning data manning would deny the society a chance to reap from the numerous advantages that come with the practice. While data mining data mining may have issue of unethical practice, banning it would do much harm than good to the society by washing down its immense benefits. However, data mining will do better than just supporting business in improving their competitiveness and improving consumer experiences. It will help to create more jobs, expand computer science, and also help government in proper planning.
References
Ahlemeyer-Stubbe, A., & Coleman, S. (2014). A practical guide to data mining for business and industry. Wiley.
Brown, M. S. (2014). Data mining for dummies. For Dummies, a Wiley Brand. John Wiley & Sons.
Loebbecke, C. (2019). Digitization, Big Data Analytics, and Artificial Intelligence Transforming Business, Society, and Research. Moscow University Economics Bulletin, 2019(6), 9-11.
Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data mining for business analytics: Concepts, techniques and applications in Python. John Wiley & Sons.
