Pro and Anti-Data Mining

Computer Science

Data mining is a topical issue that has gained prominence in today’s world because of technology. 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). 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). 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.

The use of facial recognition technology in a surveillance system. Rising insecurity and the need to have a sophisticated system of enforcing law and order has led to the use of face recognition technology to track criminals. The technology is already explored in the opening of gates and doors as part of digitalization. The proponent of using this technology in security argues that it can help the police detect and arrest criminals with more efficiency than before (Gates, 2011).  Once they detect a suspect, the cameras send a signal to security agencies for action, hence helping to arrest criminals without taking a long time. The opposing view to the use of facial recognition technology argues that the technology is not foolproof, hence the risk of mistaken identity. Misrecognitions of the face can lead to wrong prosecution, harassment, and even killing the victim. The technology has been tested before and proven to be prone to errors. I think facial recognition technology should not be used for security surveillance by the police (Gates, 2011).  The targeted audience would be lawmakers and security experts. The security experts might raise objections to banning the use of this face recognition technology on the basis that the decision would limit the government’s ability to leverage technology to improve security.

I am leaning towards data mining as a computer expert. The topic is related to my computer science degree. I have strengths in data analysis tools such as Hadoop, SQL, NoSQL, and SAS. The knowledge in this area is needed in managing data mined, hence useful. I also know programming languages such as Java and Python that are relevant to experts in data mining (Shmueli et al., 2020). However, the challenges I am likely to encounter include limited knowledge in statistics, which is useful to data mining. I am also likely to face challenges in designing and developing apps to use in data mining. I am also likely to encounter the problem of anti-data mining views. Data miners are usually seen as persons who breach privacy by stealing users’ information. I am also likely to face a challenge about the safety of data mined to ensure that it is not used to the detriment of the sources.


Gates, K. (2011). Our biometric future: Facial recognition technology and the culture of surveillance. New York University Press.

Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data mining for business analytics: Concepts, techniques and applications in Python. Wiley.

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