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Data mining can be defined as the process through which crucial data patterns can be identified from a large quantity of data. Data mining finds its applications in different industries due to a number of benefits that can be derived from its use. Various methods of data mining include predictive analysis, web mining, and clustering and association discovery (Han, Kamber and Pei, 2011).
Each of these has a number of benefits to a business. In predictive analysis, analytical models are used to deliver solutions. Using this model, a business can uncover hidden data which can be utilized for the purposes of identifying trends and therefore, predicting the future.
This method requires a business to define the problem before data can be explored. There is also development of predictive models that must be tested. Finally, these models are applied in the population identification and in the prediction of behavior. The process followed helps a business to identify its current position in relations to the industry (Simsion and Witt, 2004).
From this, businesses can plan on how best they can improve their positions in relation to other companies in the industry. The trends obtained from analysis of the acquired data can be used for the purpose of planning which might further give a company an edge over its competitors.
In association discovery, the main aim is to discover correlation among different items that make up a shopping basket. The knowledge of these correlations is important in the development of effective marketing strategies. This is possible due to the insight gained on products that customers purchase together.
This method of data analysis can also help retailers in the design layout of their stores. In this layout, the retailer can conveniently place items that customer purchase together in order to make the shopping experience interesting to customers as well as increasing chances of high sales (Kantardzic, 2011). The method can also be used by a business to determine the products they should place on sale in order to promote the sale of items that go together with the first one.
Web mining is the process through which data present in the World Wide Web or data that has a relationship with a given website activity is made available for various business purposes.
This data can either be the contents of web pages found in various websites, profiles of website users, and information about the number of visitors in a given website among others. Web mining can be used by a business to personalize its products or services in order to meet specific needs of the customers. This is possible through tracking the movement of a given target customer on various web pages.
The method can also help a business improve on its marketing strategies through effective advertising. This can be achieved when used together with business intelligence. It also helps a business to identify the relevance of information present in its web sites and how it can improve this information with the view of increasing its visibility in the market.
Clustering involves grouping of data into specific classes based on specific characteristics (Han, Kamber and Pei, 2011). The process helps in the discovery of specific groups that the business should focus on. The method also helps a business to provide specific information that can be used to win over a given class of customers.
Data mining follows a sequence that ensures the data mined meets the requirements set down by the person mining it. Different algorithms handle the process of data mining differently based on the content of the data to be mined. Therefore, the reliability of the data obtained depends highly on the method used and the nature of data. Speed of data mining process is important as it has a role to play in the relevance of the data mined.
Therefore, a given algorithm should support speedy mining of data. The accuracy of data is also another factor that can be used to measure reliability of the mined data. For this reason an algorithm should be able to use specifications issued in the process of data mining. The two requirements for reliability are met by most algorithms which make them to be reliable for the purposes of data mining.
Various concerns arise over data mining and include invasion of privacy, ethics and legality. The issue of privacy arises when private information is obtained without the consent of its owners. Application of such information for business purposes can have detrimental effects to the business. Ethical issues arise when information mined is used by a business to take advantage of the owner of such information (Kantardzic, 2011).
There is also the question of legality of data mining without the consent of the person owning such information. To address the issues above, some businesses request permission from people before they can use information on them for various purposes which must be disclosed to the person.
Predictive analysis is used by businesses in market segmentation, analysis of the shopping basket and the planning of demand. Market segmentation enables a business to serve a given market better than if it had to serve a diverse market. In shopping basket analysis, a business can easily identify the products that are needed at specific times. The business can also determine demand and effectively plan how to meet it.
References
Han, J., Kamber, M. and Pei, J. (2011). Data Mining: Concepts and Techniques. Amsterdam: Elsevier
Kantardzic, M. (2011). Data Mining: Concepts, Models, Methods, and Algorithms. New York: John Wiley & Sons.
Simsion, G. C. and Witt, G. C. (2004). Data Modeling Essentials. Massachusetts: Morgan Kaufmann
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