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3. What makes data mining an important business tool? What types of information does data mining produce? In what type of circumstance would you advise a company to use data mining?
Data mining is a term used in information management to define the analysis of large amounts of data to extract useful information chunks from this data (Laudon and Laudon 531). This activity requires the availability of large repositories of business information, such as the inventory data that companies collect to keep track of the movement of goods in a particular company. The potential gains for companies that choose to utilize data mining make it essential to understand the business environments in which this tool can prove useful. This analysis seeks to contextualize data mining to highlight the situations in which data mining can prove beneficial to a company’s activities and the kind of information that businesses can expect their data mining operations to produce.
The key element that underlines the importance of data mining as a business tool is its ability to improve the quality of the decisions that businesses make with a notable reduction in the risk associated with making these decisions. In their research, Nakash et al. (816) found that data mining through web scraping is instrumental for retailers, helping them to understand their target markets and improve their retail processes based real-time and aggregated data. For the retailers, it involves the analysis of huge amounts of raw data to draw correlations and present them in a way that the human element of the business can understand and use to inform their future decisions (Gandomi and Murtaza 138).
Data mining can have various forms of output, the key of which is the distinction between arbitrary events and those that have associations that link them to discrete elements such as individual customers or business events (Gandomi and Murtaza 143). The products of data mining also include the patterns that can indicate groupings of items by inferring to existing rules to determine the classification of new additions. This approach also features as a primary means for analyzing sequences, with the introduction of a time element also helping to produce data correlations that can anticipate the future occurrence of these events in the form of predictive analytics (Nakash et al. 819).
Information obtained through data mining can prove essential for companies whose operations generate large amounts of data since such activities can reveal relationally relevant data that the company can use to draw correlations between different data sets (Laudon and Laudon 534). These needs prove especially relevant for logistic companies, whereby deep analysis of factors such as delivery times and routing information can enhance the company’s capacity to optimize its operations. Another possible application is the healthcare industry, where correlations between non-confidential patient data can also help hospitals to understand trends in areas of vital concern such as in tracking infections in the communities that they serve.
The large amounts of data available in modern business information systems provide opportunities for companies to use this data to improve their internal processes. Data mining is elemental for businesses that have such data available, enabling them to analyze correlations between data sets as well as patterns of event occurrence among other data points. The data volumes required for these analyses can prove elemental for large companies as evidenced by the successful use of data mining by online retailers. Given the potential benefits of this tool, it is thereby essential for companies that can collect large data volumes to utilize data mining to enhance their awareness of internal and external factors that influence the success of their business.
Gandomi, Amir, and Murtaza Haider. "Beyond the Hype: Big Data Concepts, Methods, and Analytics." International Journal of Information Management 35.2 (2015): 137-144.
Laudon, Kenneth C., and Jane Price Laudon. Essentials of Management Information Systems. Upper Saddle River: Pearson, 2015.
Nakash, Jawahire, et al. "Real Time Product Analysis using Data Mining'." International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 4.3 (2015).
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