Benefits of Data Mining

selective development dig is defined as a go that riding habits statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful breeding and subsequent fellowship from large info homes, including info w behouses (Turban & Volonino, 2011). The schooling identified using data exploit includes patterns indicating trends, correlations, rules, similarities, and utilise as hazardive analytics. By employing estimateive analytics, companies are real able to understand the behavior of nodes. prognosticative analytics examines and sorts data to find patterns that sidle up client behavior.The important behavioral patterns are those that indicate what guests have responded to and lead respond to in the future. Also, patterns screw indicate a client base that is in jeopardy with the company, clients that are not company-loyal and are intimately lost. Predictive analytics of customer behavior nominate be of great bring in to the short letter (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 mouthful and are founder 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 centralise on takes the guesswork out of marking and product development. Taking the discipline retrieved and using it correctly will only increase profits (Advantages, 2012). experience discovery using data digging furnishs a huge benefit to companies. Association discovery is finding correlations or relationships between variables in a large database. For congressman, in terms of a supermarket, it is finding out that customers who subvert onions and potatoes together are also highly likely to buy hamburger meat.These correlations where one set of products predict the buying of another is referred to as associations. i nfo digging can employ association discovery allowing business to predict buying patterns and allow for more trenchant operations management and can better pinpoint marketing strategy of coupons and incentives (Association Rule 2012). Web excavation is another aspect of data dig. Web mining uses the data hive a government agency on the Internet to analyze customer data and gather information just to the company.Any time someone visits a website, uses a inquisition engine, clicks on a link, or makes an electronic transaction data is generated present to analytics. Companies use web mining to gain customer preference and insight. The information gathered is use to improve websites and create a better drug user experience for the customers. Web mining can also be used alongside of prophetical analytics. For example, on e-commerce sites every transaction is canvas. When a customer clicks on a product, web mining tools can present a list of products he/she may also be intereste d in because of other customers with the similar buying interests/habits.This tool can be passing effective in gaining business intelligence of the buying habits and preferences of customers (Turban & Volonino, 2011). info mining also employs clustering to find related 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 usually the first step. It identifies similar information and groups them to be notwithstanding examined. Customer information and demographics are an example of these clusters.The group characteristics are analyzed against desired outcomes to understand the buying habits of customers and what marketing campaigns will enhance customer response (Ali, Ghani, & Saeed). Reliability of Data Mining The benefits of data have been examined, only when it is important to look possible implications as well. Data mining uses algorithms to predict patterns and customer behaviors. Constant measures 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.Data gathered from different sources can potentially be t or even conflicting. This greatly affects the legality and resolve of algorithm, especially predictive analysis. It could alter the customers historical 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).Privacy Concerns of Data Mining. maven of the major disadvantages of data mining is the secrecy associates associated with the technique. Three major pri vacy repairs raised by consumers are identity thieving, vilify of personal information, and the well-favored brother is watching you feeling (Orwell, 1954). The first concern is identity theft. With the increase trend of e-commerce and electronic funds, identity theft has been a huge issue. The cut back amount and speed of information processing through data mining has led to a rise in identity theft devising this valid concern. The information could easily fall into the hands of anyone (Exforsys Inc, 2006).The 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 exact concern of consumers. Consumer information can certain(p)ly be misused, exploited, or for discrimination making this a valid concern (Adv antages, 2012). The last concern addressed in this paper is the summate loss of privacy, feeling controlled or watched.The government uses data mining to tail patterns of criminal activity have considered using the technique to track the feces of nation. Some people feel this goes too far, and not giving the consumer the option of having his/her information in the database takes away personal freedom. This concern is tied into the misuse of information because what stops companies to selling information to governmental or mystic agencies with the sole purpose being to control or watch an individual. With the inconstant nature of crime, and the increasing use of technology by government agencies, this concern is also valid (Advantages 2012).Measures have been taken to alleviate these concerns. Companies that utilize data mining are required to take certain actions that protect their customers privacy. One of these actions is to remove and identity related attributes from eac h customer record before the data is transferred to analysts. Also banks allow for identity theft protection services to alleviate the concern of monetary security. All of these concerns are still important and steps will have to be incessantly made and adjusted to protect the security and privacy of personal and financial information (Li & Sarkar, 2006).Real 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 tycoon recount. HyperActive Technologies developed a system that allowed cameras to track vehicles pulling into the park lot and track customers through the entire ordering process. victimisation predictive analysis of the data gathers from the cameras, the restaurant was able to conclude that at lun ch 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 passing play start in food production cutting down on wait time for customers and increasing overall productivity (Turban & Volonino, 2011). Another example of a company that uses predictive analysis is that of INRX, the leading provider of occupation information. INRX uses data mining by evaluating real time traffic measuring traffic problems and congestion.This data is collected from road censors, toll tags, traffic ensuant data, and commercial vehicles equipped with a GPS that continuously report their speed and location. Using predictive analytics, the data is studied 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 unnece ssary 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 refer points. Data collected includes historical purchases to discover trends, anticipate customer behavior, and fit 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 holding increased by over fifteen percent after the murder of predictive analytics solidifying its effectiveness (Turban & Volonino, 2011).As shown through academic seek and real world examples, data mining is a real and effective way of predicting 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. I f 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.ReferencesAdvantages 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 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.htmExforsys 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 i n 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 operation (8th ed.). New Jersey John Wiley & Sons, Inc.