Data Mining
Data Mining
Assignment 4
Shauna N. Hines
Dr. Progress Mtshali
Info Syst Decision-Making
December 7, 2012
Benefits of Data Mining
Data mining is defined as “a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban & Volonino, 2011). The information identified using data mining includes patterns indicating trends, correlations, rules, similarities, and used as predictive analytics.
By employing predictive analytics, companies are actually able to understand the behavior of customers. Predictive analytics examines and sorts data to find patterns that highlight customer behavior. The important behavioral patterns are those that indicate what customers have responded to and will respond to in the future. Also, patterns can indicate a customer base that is in jeopardy with the company, customers that are not company-loyal and are easily lost. Predictive analytics of customer behavior can be of great benefit to the business (Turban & Volonino, 2011). Companies are able to build specific marking campaigns and models such as direct mail, online marking, or media marking based on customer preference and are better able to sell their products to a more targeted customer base. Knowing what the customer wants, what they will respond to, and which customer base to focus on takes the guesswork out of marking and product development. Taking the information retrieved and using it correctly will only increase profits (Advantages, 2012).
Association discovery using data mining provides a huge benefit to companies. Association discovery is finding correlations or relationships between variables in a large database. For example, in terms of a supermarket, it is finding out that customers who buy onions and potatoes together are also highly likely to buy hamburger meat. These correlations where one
Assignment 4
Shauna N. Hines
Dr. Progress Mtshali
Info Syst Decision-Making
December 7, 2012
Benefits of Data Mining
Data mining is defined as “a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban & Volonino, 2011). The information identified using data mining includes patterns indicating trends, correlations, rules, similarities, and used as predictive analytics.
By employing predictive analytics, companies are actually able to understand the behavior of customers. Predictive analytics examines and sorts data to find patterns that highlight customer behavior. The important behavioral patterns are those that indicate what customers have responded to and will respond to in the future. Also, patterns can indicate a customer base that is in jeopardy with the company, customers that are not company-loyal and are easily lost. Predictive analytics of customer behavior can be of great benefit to the business (Turban & Volonino, 2011). Companies are able to build specific marking campaigns and models such as direct mail, online marking, or media marking based on customer preference and are better able to sell their products to a more targeted customer base. Knowing what the customer wants, what they will respond to, and which customer base to focus on takes the guesswork out of marking and product development. Taking the information retrieved and using it correctly will only increase profits (Advantages, 2012).
Association discovery using data mining provides a huge benefit to companies. Association discovery is finding correlations or relationships between variables in a large database. For example, in terms of a supermarket, it is finding out that customers who buy onions and potatoes together are also highly likely to buy hamburger meat. These correlations where one
References: Advantages and disadvantages of data mining (2012). Retrieved December 9, 2012 from http://www.dataminingtechniques.net/data-mining-tutorial/advantages-and-disadvantages-ofdatamining/ Ali, R., Ghani, U., & Saeed, A. (n.d.) Data clustering and its applications. Retrieved December 5, 2012 from http://members.tripod.com/asim_saeed/paper.htm Data mining: issues. (n.d.) Retrieved December 7, 2012, from http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/ issues.htm Exforsys Inc. (2006). Data mining privacy concerns. Retrieved December 5, 2012 from http://www.exforsys.com/tutorials/data-mining/data-mining-privacy-concerns.html Li, X. & Sarkar, S. (2006) Privacy protection in data mining. Retrieved December 6, 2012 from http://dl.acm.org/citation.cfm?id=1245621 Turban, E., & Volonino, L. (2011). Information technology for management improving strategic and operational performance (8th ed.). New Jersey: John Wiley & Sons, Inc.