Information Systems for Decision Making
10 December 2013
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
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 likes to feel like they are being spied on. Another concern can be the worth of the data. The predictions are only as good as the data that it is presented with, as with other information systems, it is based on garbage in, garbage out.
Predictive Analytics Benefits
Predictive analytics sorts through the data that is collected and analyze the data for patterns from the customers. Then it will make suggestions based on the data of what the customer might buy next or not be interested in buying (Turban & Volonino, 2011). After the analysis it can be used to understand the behavior of the customers. There are many benefits that business can receive from predictive analytics. The following information can be beneficial to a business that comes from predictive analytics; the customers who respond to new products, and who respond to discounts, who buy specific product categories, which customers are most loyal, and which customers might not be using the business very much longer. This information is beneficial because, it can alert a business to when they might be losing a customer, and it gives the business the opportunity to reward the current and most loyal customer. It gives the business the opportunity to contact the customer that it might be losing and try to win the customer back or get the reason they are no longer using the business. It gives the business an idea of what products are more likely to sell when and they can plan accordingly. They can get more hot weather clothes in the warmer months because that’s when it has been predicted that customers buy hot weather the most and offer less cold weather clothes at the same time. IT would prevent businesses having overstock of items because the items didn’t sell because the items were offered at the wrong time of year.
Association discovery can be defined as analyzing data into relationships while sorting through a massive amount of data. Association discovery finds products of...
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,
Patterson, L. (2010, APR 27). The nine most common data mining techniques used in predictive
(Turban & Volonino, 2011)
Two Crows Corporation (1999) Introduction to Data Mining and Knowledge Discovery,
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