In the digital age, when technological progress and innovations make up an essential part of human lives, people find themselves in need of managing a complex and rapidly evolving world of information and technology. As people rely more and more on electronic devices in their everyday lives, they generate more and more digital data. To store, measure, and analyze this data requires equipment, trained professionals, and resources that allow effective use of information extracted from the overwhelming amount of raw data. Obtaining valuable patterns or observations from large data sets with different methods is called data mining. In a technology-driven world that evolves with remarkable speed and produces new trends that affect our lives at every level, it becomes increasingly crucial for business owners to implement data mining techniques.
For small and midsize companies, data mining is needed to optimize business processes and keep their enterprises competitive. Today, companies from across industries apply data mining to adjust their production, marketing campaigns, to succeed on the market. Data mining is used by retailers, small and large media outlets, logistic companies, insurance companies, and other enterprises to identify and act on new business opportunities (Shmueli, Patel, Bruce, et al., 2018). Nevertheless, despite the obvious advantages of applying data mining in business, there are certain disadvantages of doing it as well, particularly relevant for smaller enterprises in the initial stage. Considering that data mining can be expensive, a company’s management should evaluate all possible pros and cons of applying it for their enterprise.
Pros and Cons of Data Mining
In general, analyzing large amounts of data produced by clients allows us to see new business opportunities and approach them in a highly advanced manner. For example, tracing clients’ purchasing preferences can help to design and introduce a new product. Marketing strategies, which are typically tied to systemic statistical analysis of consumer behavior, can become more profitable with data mining by proposing new communication channels or other methods to approach current and potential customers (Shmueli, Patel, Bruce, et al., 2018). Other benefits of using data mining techniques include strengthening brand strategy, predicting market trends, being ahead of the competition, and even helping to determine criminal activity.
As for essential cons of using data mining, these mainly include its high price, as data mining requires technology to store data and work with it, and the work of professionals who can evaluate the results and the means they can be applied within a company. Apart from the high price of data mining, other limitations arise from general privacy concerns. Companies can collect and hold vast amounts of sensitive private information about their customers when monitoring their consuming habits. This information can be hacked, sold, or leaked, implying additional risks and costs for an enterprise (Shmueli, Patel, Bruce, et al., 2018). Regardless of the numerous advantages data mining can bring to a company, the shortcomings are needed to be considered by the management as well to launch a data mining project effectively.
Data Mining Tools
Data mining combines statistics, machine learning, and artificial intelligence to derive valuable patterns of information from big data. Data mining tools include modern software that processes big data sets and tracks patterns and trends. Such software can be free, like RStudio, or be high cost like Neural Designer. With growing interest in data mining, its growing importance in the economy, and increasing amounts of data generated by people, data mining software has been one of the fastest-growing fields in software engineering in the past decades (Shmueli, Patel, Bruce, et al., 2018). Even large corporations can lack technological capacity or personnel to execute data mining projects independently, just by using special software products, redirecting this task to a third party.
Handing data mining issues to a consulting company can be an optimal solution especially for a midsize enterprise, because such companies offer specialists, who can provide high-level of expertise, making data mining available for companies from any industry. The largest data mining consulting vendors include such firms as IBM, EXL, or CBIG Consulting (Shmueli, Patel, Bruce, et al., 2018). With offices across the world, these consulting companies suggest the global importance of data mining services.
Data Mining in Practice
Traditional businesses, retailers, and manufacturers can benefit from data mining, which, depending on the company’s industry, can be a crucial practice needed to optimize their business process, acquire more customers, and be ahead of their competition. Small and midsize enterprises make up a significant part of economies in most countries, and their ability to apply data mining can determine their success among competitors. Other companies, particularly high-tech start-ups, emerge from available data that they can use (Akinkunmi, 2018). One of the most successful business using data mining – Amazon with a massive amount of data. Worth noting Hearby, a Boston-based company, uses public datasets and maps to amplify live music globally. For some emerging brands in the beauty industry, data mining can pave the way to luxury stores by directly responding to their customers’ needs with a new product.
It is hard to overestimate the importance of data in today’s global economy. Data mining processes, tools, and techniques make up a crucial part of a modern market, more and more tied to the world of technology and information. To fit in the world of commerce of the digital age, companies can carry out data mining projects independently, using the software products designed for businesses, or redirect this task to a third-party vendor, making data mining easier. Regardless of the ways how data mining is carried out, before launching a data mining project, the company’s management should take into account both its potential advantages and disadvantages, such as high price.
Akinkunmi, M. (2018). Data mining and market intelligence. Morgan & Claypool Publishers.Shmueli, G., Patel, N. R., Bruce, P. C., Yahav, I., & Lichtendahl, K. C. (2018). Data mining for business analytics: concepts, techniques, and applications in R. Wiley.