Data Mining
Data Mining
Information Systems for Decision Making
10 December 2013
Abstract
Data mining the next big thing in technology, if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining.
Keywords: data mining, benefits, privacy concerns
Data Mining Benefits of Data Mining for a Business
Data mining can be explained as the process of a business collecting data on their customers or potential customers to increase customer business. A business will collect data on their customers or potential customers and use that data to give them coupons, promote sells, and analyze buying and selling trends. Data mining can benefit the customer as well as the business. Data mining can be used in the retail industry, the finance industry, and the healthcare industry. Any industry can benefit from data mining but those are the top three (Turban & Volonino, 2011). Data mining is a way for large businesses to get to know their customers. The information gathered from data mining can let a large company learn what their customers want and how they want it. It can also benefit large companies get to know their employees, the company can learn how to satisfy their employees and then they might work better. Showing employees that a big company knows a little bit about them gives the impression that the company cares. When employees think that the company cares they tend to work better.
With processes that have benefits, there are also some concerns. Privacy is a concern of the customers involved. Also, customers are concerned with what is being done with the data that is collected and how the businesses are collecting the data. No one
Information Systems for Decision Making
10 December 2013
Abstract
Data mining the next big thing in technology, if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining.
Keywords: data mining, benefits, privacy concerns
Data Mining Benefits of Data Mining for a Business
Data mining can be explained as the process of a business collecting data on their customers or potential customers to increase customer business. A business will collect data on their customers or potential customers and use that data to give them coupons, promote sells, and analyze buying and selling trends. Data mining can benefit the customer as well as the business. Data mining can be used in the retail industry, the finance industry, and the healthcare industry. Any industry can benefit from data mining but those are the top three (Turban & Volonino, 2011). Data mining is a way for large businesses to get to know their customers. The information gathered from data mining can let a large company learn what their customers want and how they want it. It can also benefit large companies get to know their employees, the company can learn how to satisfy their employees and then they might work better. Showing employees that a big company knows a little bit about them gives the impression that the company cares. When employees think that the company cares they tend to work better.
With processes that have benefits, there are also some concerns. Privacy is a concern of the customers involved. Also, customers are concerned with what is being done with the data that is collected and how the businesses are collecting the data. No one
References: Armonk (2010) IBM: Memphis Police Department Reduces Crime Rates with IBM Predictive Analytics Software, http://www-03.ibm.com/press/us/en/pressrelease/32169.wss Oracle.com (2008) Oracle Data Mining Concepts, http://docs.oracle.com/cd/B28359_01/datamine.111/b28129/clustering.htm Patterson, L. (2010, APR 27). The nine most common data mining techniques used in predictive analytics (Turban & Volonino, 2011) Two Crows Corporation (1999) Introduction to Data Mining and Knowledge Discovery,