Data mining generally regarded as knowledge discovery is the process of analyzing data and summarizing into useful information. It is the process which is gaining trend in the current market structure. Most of the businesses are embracing the new technology with the aim of increasing the sales and cutting down the operational cost (Berry et al., 2004). Technically, data mining has explicitly been useful in the management of the activities of the business. It analyzes data from different dimensions and finds the correlation which exist among the data. The conclusion which is normally arrived at after continuous data analysis is used to strategize the activities of the business. Continuous development and innovations in the computer software has dramatically increased the accuracy of the data analysis. For example, one of the Grocery in Midwest grocery used data capacity from Oracle to analyze the buying trends. It was observed when men bought diapers on Thursday and Friday, they also bought beer. They concluded that men bought beers for the weekend. This information could have assisted the owner to change some of the organization in the grocery (Larose, 2005). Probably they could have placed the beer near the diapers to for display. This move would in a larger magnitude increase the sales of the beer. Data mining can be classified into four sections. These are clustering, classification, association rule and finally feature extraction. The uncovered information can be used by the business to detect fraud, market analysis as well as market segmentation (Han et al., 2006). These are paramount features that require constant check for the business entity to succeed. With the current transformation in the market structure, organizations are in constant competition to outdo the others. There exist constant rivalry which is necessitating any business to put in place solid data backup systems which would assist them survive in the market. Large institutions which are anticipating growth have a task to use data mining to safeguard their sales and reputation.
To justify your answer properly by giving suitable examples why there is a need to apply data mining process on the application you identified.
The applications identified are fraud detection, market analysis and market segmentation. For as business to thrive in a diverse market, it should be ready to solve the problems which arise appropriately. Failure to do so would result to business failure which is not the core mandate of any existing business. For example, it is essential for the business to manage its data well else the fraudsters access their data (Witten et al., 2005). What it means is that the business will be at risk since some of them information pertaining their operation will be accessible to other people illegally. Confidential data such a financial inflow within the organization requires to be personal. The competitors should not be exposed to such information. Moreover, some of the fraudsters hack into the system of the organizations and distort data which is of importance. This results to fake information spreading to the customers which may eat away the reputation of the company. Having a good data mining system would assist to combat the crime (Motoda, 2002). Additionally, this system has an impact of analyzing the market structure and segmentation. The organization can be able to compare its performance with other existing businesses in the market. In consequence, they are able to identify their weaknesses and resolve them in accordance to the requirement. Fostering the growth is the objective of every organization. To achieve such a goal, the is need to carry out a thorough market search. Accompaniment of the search with statistical analysis of then data gotten helps the management to determine the state of then business (Knobbe et al., 2006). Customers trend will be determined and tailor good in respect to their demands.
To develop a suitable data mining process based on previous available processes used in fulfilling the business requirements identified for the process in a corporate.
Most organizations have been exposed to many algorithms sued for data analysis hence finding it hard on what to choose. When inappropriate algorithm is used, it may results to unnecessary data gotten which is not useful in decision making. It is necessary to use the right algorithm to arrive at the correct information useful to the organization at large (Berry et al., 2004). In order to develop suitable data mining process, understanding the problem is the key feature that requires consideration. The organization must understand the problems that surrounds it. Secondly, creating target data set will enable the organization to analyze the information of their interest. Before developing any data mining process, it is necessary to consider the factors that influence data mining. The structure of the data and main goal of the problem should be the topmost considerations. However, incorporation of then system without any plans will have more detrimental effects than it would be expected (Larose, 2005). Moreover, familiarity with the algorithm to be used is another key factor which should not be negated. The organization should have the right experts in place to help implement the process. The process identified below can be used for data mining.
Berry, M. J. A., & Linoff, G. (2004). Data mining techniques: For marketing, sales, and customer relationship management. Indianapolis: Wiley.
Larose, D. T. (2005). Discovering knowledge in data: An introduction to data mining. Hoboken, N.J: Wiley-Interscience.
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