Data Mining
Today, companies of all sizes and types are eager to learn as much about their customers and competitors as possible in order to gain and sustain a competitive advantage in an increasingly globalized marketplace. One strategy that has demonstrated utility for this purpose is data mining. The purpose of this paper is to provide a review of the relevant literature to determine the pros and cons of using data mining for a medium-sized business operating in the U.S., a discussion about the tools that are available for this purpose and their respective costs, and an assessment concerning the amount of time it would it take to be up and running with data mining. Finally, an analysis concerning whether a third-party vendor make it easier to data mine and two examples of businesses that successfully use the data mining process are followed by a summary of the research and key findings concerning these issues in the conclusion.
What are the pros and cons of using data mining?
One of the major pros of using data mining is the fact that this research strategy draws on existing data to develop new findings and insights that might not otherwise be possible. For instance, according to the definition provided by Chang (2022), data mining refers to the nontrivial process of extracting implicit, unknown, and potentially useful information from a database or data warehouse (p. 1). In laymans terms, the major benefit refers to the use of a computer-based application to transform raw data into meaningful information (Data mining in business analytics, 2022). By analyzing patterns and anomalies in big data, data mining can help business practitioners make informed decisions and develop timely strategies in response to newly identified opportunities or potential threats in ways that promote organizational efficiency and profitability (Data mining in business analytics, 2022).
Although data mining applications can achieve these benefits, the process is not without challenges and one of the major cons in using data mining relates to the paucity of relevant research concerning the manner in which successful firms have leveraged the findings that emerge from data mining analytics into real-world initiatives. In this regard, Zhan et al. (2019) point out that, While providing high-level evidence of these benefits, studies have failed to systematically investigate the specific mechanics behind how firms...
…described above. Likewise,
outsourcing this function to a third-party vendor may also make good business sense depending on a cost-benefit analysis.
Two examples of businesses that successfully use the data mining process
Two prominent examples of businesses that currently use data mining to good effect include Amazon which has routinely collected customer data as well as its competitors pricing data and McDonalds which collects sales data from its tens of thousands of restaurants to identify the quality of the customer experience and opportunities for improvement (Peterson, 2016).
Conclusion
The research showed that data mining applications search large databases to generate new business-related findings and insights that might not be otherwise possible to detect. The research also showed, though, that achieving the full range of benefits that can accrue to data mining is a challenging enterprise that demands the right mix of expertise and data selection. In addition, although proprietary data mining products are expensive, open-source versions are also available depending on the unique needs of a given business. Finally, major companies such as Amazon and McDonalds fast food company has leveraged their data mining processes in ways that have helped them achieve and sustain a competitive…
