Data Mining for Business: Tools, Costs, and Real-World Uses

Introduction

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 take to be up and running with data mining. Finally, an analysis concerning whether a third-party vendor makes 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 in the conclusion.

Pros and Cons of Data Mining

One of the major advantages 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. 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 practical 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, thereby promoting organizational efficiency and profitability (Data mining in business analytics, 2022).

Although data mining applications can achieve these benefits, the process is not without challenges. One of the major drawbacks of using data mining relates to the scarcity of relevant research concerning the manner in which successful firms have translated findings 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 can realize these benefits" (p. 6335). In addition, the extent to which organizations realize the benefits of data mining also depends on which tools they use, an issue discussed further below.

Available Data Mining Tools

Given its proven ability to provide business practitioners with the information they need to make informed decisions in a complex environment, it is not surprising that numerous commercial data mining applications are available. There are also powerful open-source versions, including the following, which are among the most commonly used at present:

RapidMiner: This proprietary application provides conventional data mining tools as well as the capability to learn from the findings the process generates over time, all in an integrated fashion. RapidMiner also features an intuitive user interface that facilitates its use from the outset and assists with (1) data modeling and preparation, (2) data cleansing, (3) exploratory data analysis, and (4) visualizations.

Oracle Data Mining: This is a leading proprietary data mining product that includes a wide array of functions, including multiple data mining algorithms used to classify, regress, predict, and identify anomalous outliers. An important benefit of this product is the customer support provided by expert Oracle technical staff.

Apache Spark: This open-source data mining application comes complete with many of the same features offered by the platforms described above, as well as the ability to build parallel applications to promote greater integration. Some indication of this application's popularity is the fact that nearly 13,500 companies currently use Apache Spark for all of their data mining needs (Sarangam, 2020).

Conclusion

The research showed that data mining applications search large databases to generate new business-related findings and insights that might not otherwise be possible to detect. The research also showed, however, that achieving the full range of benefits that can accrue from data mining is a challenging enterprise that demands the right mix of expertise and data selection. 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 McDonald's have leveraged their data mining processes in ways that have helped them achieve and sustain a competitive advantage.

References

Chang, R. (2022). Evaluation model of enterprise lean management effect based on data mining. Discrete Dynamics in Nature & Society, 1–11.

Data mining in business analytics. (2022). Western Governors University. Retrieved from

Peterson, R. (2016, November 7). Twenty companies do data mining and make their decisions better. BarnRaisers. Retrieved from https://barnraisersllc.com/2016/11/07/companies-data-mining-business-better/

Sarangam, A. (2020, December 17). Top fourteen data mining tools. Jigsaw. Retrieved from

Zhan, Y., Tan, K. H., & Huo, B. (2019). Bridging customer knowledge to innovative product development: A data mining approach. International Journal of Production Research, 57(20), 6335–6350.