Data Mining, a Major Asset for Companies & Computer Science

Data Mining, a Major Asset for Companies & Computer Science

Introduction

Data mining is a new area of interest to companies, governments, and computer science because of its impact in revolutionizing the world. Decision-makers and planners continue to demand more accurate information to support them in making choices. The tech-based firms have turned to data mining because of its lucrative nature, underscoring the area as a new frontier in business competition and general advancement of management (Gao.gov, 2021). Data mining is a practice of extracting mega data, discovering and analyzing trends, and using it to set out further specific goals (Ahlemeyer-Stubbe & Coleman, 2014, p. 17). Data Mining is Major Asset for Companies and computer science because of its huge benefits in business intelligence, marketing, and demand for experts in computer technology.

Arguments for Data Mining

Data mining is a major asset for commercial firms because it helps them to gain broader and accurate business intelligence than it was when using traditional tools. According to Shmueli et al. (2020), business intelligence is no longer reliant on humans to collect and analyze primary data. Much has changed with the advent and use of computer and digital technologies. Instead, 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 (Shmueli et al. 2020, p. 21). Major social media firms such as Facebook capitalize on data mining to market their product to the right customers, enhancing the marketability of companies’ products and services (Soares & Ghani, 2010, p. 41). In addition, business forecasting has been made easier and reliable with the use of data mining. According to Ahlemeyer-Stubbe and Coleman (2014), businesses can accurately predict the market and align their plan with the expected changes. Consequently, this highlights the great value of data mining in improving the quality and access to business intelligence, which most big companies rely on for strategic planning and marketing.

Moreover, data mining is a major asset for companies and computer science because it is growing in demand. Shmueli et al. (2020) establish that the demand for data mining-related services and intelligence has recorded massive growth in the last three years – 20 times more than in the previous years (Shmueli et al., 2020, p. 4). The growth has seen an increase in the need for experts in programming langue, statistics, web designing, and general computer science. The growing need for these experts is a blessing to computer science because it has led to the expansion of the field, bolstering its place in the current society. Although there have been concerns about the future of technology in society, organizations and individuals have shown resilience in absorbing technology and expanding its use (Loebbecke, 2019, p. 10). Technology has become part and parcel of everyday life, making it hard for those opposed to its use to hold on to their positions for long. The increase in demand for data mining in recent years is linked to its huge value in supporting decision-making through collecting reliable information and intelligence. It is important to note that even governments have turned to data mining to help them in security and general management because of the immense benefit of the practice (Gao.gov, 2021). Therefore, it is evident that the value of data mining in science and companies is based on its contribution to the increased demand for computer science experts and business intelligence.

Data mining is also an asset for science because it has made research work easier, cheap, and credible. Researchers can rely on data mining techniques to collect accurate data from targeted populations (Loebbecke, 2019, p. 32). The ability to collect accurate information during research work has improved the effectiveness of science in studying various phenomena. The cost of gathering and analyzing information has also been significantly reduced to the benefit of science. Initially, research work was very expensive because it relied on manual techniques to gather data for analysis. In this respect, data mining has revolutionized scientific research to benefit science and general society.

Data mining has revolutionized competition between companies, underlining its value in the strategic development of firms. Companies that cannot embrace technology and leverage it to compete in the modern business world are usually overtaken by their competitors that embrace technology. One of the greatest assets to business today is business intelligence gathered through technology. Data mining is the heart of digital intelligence, for it enables computers to access a huge and broad range of useful information about market trends that they would have otherwise not accessed (Shmueli, Patel & Bruce, 2007, p. 35). The information helps them to properly plan on aspects such as marketing, expansion, and production. Heightened competition is good for improving the quality of products and services offered to consumers. At the same time, it can drive prices down to the benefit of the general public (Shmueli et al., 2020 pp. 67-68). Consequently, this evidence supports the claim that data mining is an asset to companies by highlighting the value of the practice in enhancing and supporting the competitiveness of businesses.

Arguments against Data Mining

As much as there are numerous advantages related to data mining, some negative issues have to be mentioned. First, data mining requires specialized skills and sophisticated technology, and this may be a limitation as not any other person can conduct data mining. The other shortcoming is related to data privacy, where the data gathered may be exposed and used for purposes not intended. There is also the issue of security in which the data mining systems may be hacked and misused. Furthermore, with data mining, the accuracy of the data can be put into question as it may be inaccurate, hence being unreliable in decision-making. Finally, the other problem associated with data mining is related to information misuse. Individuals may use the data gathered for unintended purposes, and this may harm the victims (Shmueli et al., 2020, pp. 27-8).

Rebuttal

Despite the few challenges that face data mining, it can be noted that the advantages supersede the disadvantages. Most of the disadvantages can be mitigated by observing high ethical standards to ensure data safety and privacy and using the data only for the intended purposes. Also, the need for technical skills and technological investment cannot be an impediment when comparing the advantages of data mining.

Conclusion

Data mining has increasingly demonstrated to be of great value to businesses and computer science because of how it has transformed business competition, intelligence gathering and drastically increased the demand for computer experts. Firms are now leveraging big data to collect intelligence about the market and strategize on competing effectively. Computer science continues to gain prominence because of the growing demand for experts in data mining, emphasizing the great advantage of the practice of computer science. Businesses that intend to remain competitive must invest heavily in technology, particularly getting the right experts and data mining. Data mining will remain part and parcel of the future as society embraces technology to reap its numerous benefits. The disadvantages of data mining can be controlled through rules and regulations to ensure that its immeasurable advantages are protected.

References

Ahlemeyer-Stubbe, A., & Coleman, S. (2014). A practical guide to data mining for business and industry. Wiley.

Gao.gov. (2021). Data Mining: Results and Challenges for Government Program Audits and Investigations. Gao.gov. https://www.gao.gov/products/gao-03-591t.

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

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.

Soares, C. A. M., & Ghani, R. (2010). Data mining for business applications. IOS Press.

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