'Benefits of Data Mining\r'

'selective education digging is define as â€Å"a process that uses statistical, mathematical, artificial tidings, and machine-learning techniques to extract and identify useful cultivation and subsequent cognition from large entropybases, including entropy w behouses” (Turban & adenine; Volonino, 2011). The discipline identify use entropy minelaying includes patterns indicating trends, correlations, rules, similarities, and use as look toive analytics. By employing prophetical analytics, companies are in corpo surelyity able to understand the manner of guests. predictive analytics examines and sorts entropy to find patterns that highlight client behavior.\r\nThe important behavioural patterns are those that indicate what clients have acted to and allow respond to in the future. Also, patterns muckle indicate a guest base that is in jeopardy with the company, clients that are non company-loyal and are easily lost. Predictive analytics of customer behav ior throw out be of great derive to the transmission line (Turban & Volonino, 2011). Companies are able to build particular bulls eye campaigns and models such as direct mail, online marking, or media marking based on customer tasting and are dampen able to sell their products to a more targeted customer base.\r\nK at one timeing what the customer wants, what they pull up stakes respond to, and which customer base to digest on takes the guesswork out of marking and product development. victorious the knowledge retrieved and using it correctly will scarce increase profits (Advantages, 2012). Association discovery using information mining provides a huge benefit to companies. Association discovery is finding correlations or relationships betwixt variables in a large infobase. For example, in term of a supermarket, it is finding out that customers who demoralise onions and potatoes in concert are overly highly likely to buy hamburger meat.\r\nThese correlations wh ere unity set of products predict the purchasing of sepa come in is referred to as associations. Data mining prat employ association discovery allowing business to predict get patterns and allow for more strong operations solicitude and contribute better pinpoint market strategy of coupons and incentives (Association Rule 2012). Web mining is a nonher aspect of entropy mining. Web mining uses the information tranquil on the Internet to analyze customer entropy and gather instruction beneficial to the company.\r\n all time someone visits a website, uses a chase engine, clicks on a link, or makes an electronic work data is generated subject to analytics. Companies use web mining to gain customer druthers and insight. The information gathered is used to improve websites and create a better user experience for the customers. Web mining shtup also be used alongside of predictive analytics. For example, on e-commerce sites every transaction is analyzed. When a custome r clicks on a product, web mining similarlyls can present a list of products he/she may also be interested in because of other customers with the similar buying interests/habits.\r\nThis tool can be extremely effective in gaining business intelligence of the buying habits and preferences of customers (Turban & Volonino, 2011). Data mining also employs clustering to find link customer information and to provide valuable information to the company. Clustering gathers information and designates clusters of similar products and objects. In data mining, clustering is comm however the get-go step. It identifies similar information and bases them to be tho examined. Customer information and demographics are an example of these clusters.\r\nThe group characteristics are analyzed against desired outcomes to understand the buying habits of customers and what marketing campaigns will enhance customer answer (Ali, Ghani, & Saeed). Reliability of Data Mining The benefits of data have been examined, but it is important to look potential implications as well. Data mining uses algorithms to predict patterns and customer behaviors. Constant mea reals are needed to make sure the algorithms are working correctly, but the issue of reliability stems a little deeper. Algorithms and data analysis can only be as reliable as the actual data analyzed.\r\nData gathered from dissimilar sources can potentially be t or even conflicting. This greatly affects the logicality and result of algorithm, especially predictive analysis. It could alter the customer’s historic purchases or demographic information rendering the information useless and even costly. Data mining is a useful tool and should be trusted up to a point. It should not be the only solution. Companies should not only use data mining for marking and operations decisions. The costs of mistaking customer preference and predicting behavior could be catastrophic (Data Mining).\r\nPrivacy Concerns of Data Min ing. One of the major disadvantages of data mining is the cover tie ins associated with the technique. Three major privacy contacts raised by consumers are privateity theft, vituperate of personal information, and the â€Å" big(p) brother is watching you” lifeing (Orwell, 1954). The first concern is identity theft. With the increasing trend of e-commerce and electronic funds, identity theft has been a huge issue. The sheer totality and speed of information processing through data mining has led to a rise in identity theft making this valid concern. The information could easily fall into the hands of anyone (Exforsys Inc, 2006).\r\nThe second concern is the misuse of personal information. Companies gather information as specific to customer purchases, names, phone numbers, addresses, and other information then store it in a database. once obtained, copies can be made with little effort. Companies can easily sell this information to other companies. This is the consu me concern of consumers. Consumer information can certainly be misused, exploited, or for discrimination making this a valid concern (Advantages, 2012). The last concern addressed in this paper is the total loss of privacy, feeling controlled or watched.\r\nThe presidency uses data mining to impression patterns of cruel activity have considered using the technique to track the movement of people. Some people feel this goes too far, and not giving the consumer the choice of having his/her information in the database takes off personal freedom. This concern is tied into the misuse of information because what stops companies to selling information to governmental or private agencies with the sole purpose macrocosm to control or watch an individual. With the volatile nature of crime, and the increasing use of technology by government agencies, this concern is also valid (Advantages 2012).\r\nMeasures have been interpreted to alleviate these concerns. Companies that utilize data mi ning are required to take certain actions that protect their customer’s privacy. One of these actions is to remove and identity related attributes from each customer record before the data is transferred to analysts. Also banks allow for identity theft protective covering services to alleviate the concern of fiscal security. solely of these concerns are still important and steps will have to be continuously made and familiarized to protect the security and privacy of personal and financial information (Li & Sarkar, 2006).\r\nReal World Examples of Predictive Analytics Predictive analysis and how it is beneficial to companies has been discussed above in theory. To completely understand how predictive analysis is used is to look at real world examples. The first example is how a fast food restaurant used hyperactive Technologies to predict what customers might order. HyperActive Technologies developed a system that allowed cameras to track vehicles drag into the put l ot and track customers through the wide-cut ordering process.\r\nUsing predictive analysis of the data gathers from the cameras, the restaurant was able to conclude that at lunch period; approximately twenty percent of cars entering the parking lot would order at least one cheeseburger. With this information, the cooks were able to get a head jumping in food production cutting shovel in on wait time for customers and increasing boilers suit productivity (Turban & Volonino, 2011). Another example of a company that uses predictive analysis is that of INRX, the leading supplier of transaction information. INRX uses data mining by evaluating real time traffic measuring traffic problems and congestion.\r\nThis data is collected from road censors, toll tags, traffic misfortune data, and commercial vehicles equipped with a GPS that continuously embrace their speed and location. Using predictive analytics, the data is examine to determine traffic patterns at certain locations and times. Drivers now have access to real time traffic information. This information has proven to be extremely effective and useful to drivers allowing them to make better decisions and avoid excess delays (Turban & Volonino, 2011). The flower company, 1-800-FLOWERS. om, has also used data mining techniques, specifically predictive analytics. The company collects and analyses data at all contact points. Data collected includes historical purchases to discover trends, anticipate customer behavior, and realise customer needs and preferences. This technique has proven to be an effective way of increasing the response rate to customers, identifying profitable customers, and establishing customer loyalty. Customer retention increase by over fifteen percent by and by the implementation of predictive analytics solidifying its effectiveness (Turban & Volonino, 2011).\r\nAs shown through academic research and real world examples, data mining is a real and effective way of predic ting customer behavior and buying patterns. Measures need to be taken not only to overcome the stigma that data mining is unsecure and takes away personal freedom, but to make sure individual information is, in fact protected. If these measures are taken, data mining is a win-win for both businesses and consumers. Consumers will feel heard, understood, and taken care of. Businesses can actually focus resources on building that business-to-customer relationship and will be able to give the people what they need.\r\nReferences\r\nAdvantages and disadvantages of data mining (2012). Retrieved declination 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 celestial latitude 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.f rand/teacher/technologies/palace/ issues.htm\r\nExforsys 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.). wise Jersey: John Wiley & Sons, Inc.\r\n'