Defining Data Mining Definitions of data mining vary significantly in scope and inclusion or exclusion of key concepts. The most common definition includes the four types of relationships including classes, clusters, associations and sequential patterns (Han, Kamber, 2000)
CRM-based implementations of data mining often include sentiment analysis which provide insights into branding and perceptions of companies obtained through social networks (Sun, 2006). The future of data mining is going to include sentiment analysis and the ability to ascertain attitudinal data from the massive amounts of data being generated from social networks (Lai, Liu, 2009)
Evaluating Data Mining as a Strategic Technology The continual refinement of data mining from a technology to platform on which solutions for analyzing, monitoring and defining are built continues at an accelerating pace (Osei-bryson, Rayward-smith, 2009). The levels of economic uncertainty and the need companies have to compete using intelligence is one of the primary factors driving its adoption and growth (Li, Wu, 2010)
While there are major differences in these definitions of data mining, they all share the common mission of unifying the analytical, transaction and customer-based databases that are prevalent throughout organizations. Data mining applications are used for determining patterns, relationships and the relative strength or weaknesses of causality in data sets, often looking to bring greater intelligence to transaction-based records and databases (Maggioni, 2009)
Analyzing the data through the use of application software is also going through a revolution of its own today as AJAX (Asynchronous JavaScript) and XML networks are also streamlining the use of Web-based applications that are used for intensive data mining tasks. The streamlined design of AJAX application is leading to Web Services that can scale to support more of the front-end analysis at the client level of networks (Nayak, 2008)
Data Mining Evaluating Data Mining as a Strategic Technology The ability to quickly gain insights from a diverse and often incompatibles set of databases and data sets are possible when data mining techniques are used. Data mining is the process by which very large datasets are analyzed for trends, patterns, insights and intelligence not discernable from a cursory analysis of the data sets themselves through manual means (Osei-bryson, Rayward-smith, 2009)
The more mainstream definition of data mining however concentrates on the integration of disparate, often non-integrated systems together so that a single system of record can be produced upon which analysis, queries and advanced extraction can be performed (Berry, 2004). The use of Extraction, Transfer & Load (ETL) technologies and Online Analytic Processing (OLAP) are often used for creating reporting and analytical frameworks that organizations use to streamline the analysis, reporting and continual updating of databases in a data warehouse, which is used for completing data mining tasks (Rutledge, 2009)
There are also data mining applications that seek to create neural networks (Han, Kamber, 2000) that can interpolate the relationships between data elements and create causal-based models over time. Google is using data mining not only to determine how users are accessing their search engine, for the definition of personalization (Stamou, Ntoulas, 2009) and for the development of linguistic models through latent semantic indexing (Kuhn, Ducasse, Girba, 2007) which gives the search engine provider a better understanding of how to index the Internet
Examples of data mining abound in industries that have an exceptionally large amount of information they have collected form customers. This includes but is not limited to aerospace and defense (Cressionnie, 2008), auto manufacturers including aftermarket auto warranty analysis and lifetime product quality of automobiles (Buddhakulsomsiri, Zakarian, 2009), customer relationship management (Sun, 2006), eduation (Velasquez, Gonzalez, 2010), healthcare (Li, Wu, D2010) and many others
According to the graph below, the model ended up being fairly accurate, with the majority of (visible) misses underestimating the film's box office return. (Foster, 2012)] References: Chulis, Kimberly
If that audience is large enough, getting exclusive access to House of Cards makes sense. (Willmore, 2012) In this case, Netflix has altered its decision-making process and choice of investments based on data mining
Data Mining Determine the benefits of data mining to the businesses when employing: Predictive analytics to understand the behaviour of customers "The decision science which not only helps in getting rid of the guesswork out of the decision-making process but also helps in finding out the perfect solutions in the shortest possible time by making use of the scientific guidelines is known as predictive analysis" (Kaith, 2011)
This helps target in keeping a record of the past purchases by the customers. The women who were noted to have signed for the Target baby registries were sent coupons from the baby and maternity line (Hill, 2011)
In this aspect, data mining is an effective tool for helping a company to focus its resources on: those customers who are more likely to buy a particular product or service. (Graettinger, 2010) This is significant, because it shows how data mining is a useful tool in helping a company to efficiently utilize their resources on those individuals who are most likely to make a purchase from them
This is useful, because it helps them in their marketing efforts to reach out to prospects who are most interested in what they have to offer. (Reidy, 2005, pp
In the case of the drug company, this allows them to effectively focus their sales force on finding those individuals who are interested in what they have to offer, based on the most recent data that was obtained. (Saul, 2006) (Reidy, 2005, pp
For example, in the new social-media paradigm there are literally hundreds of different demographic and psychographic modifiers that might assist firms in moving from an older marketing paradigm to a new, service to the client model, all by using data mining and warehousing tools (Badroliwalla, 2009). Data mining can also be used to reduce losses within organizations by keeping a better track of inventory and waste, examining trends of usage in a way that a human could never do efficiently (Hadfield, 2009)
Improvement in computer power has also allowed for the field of artificial intelligence to evolve which also improves the sifting of massive amounts of information for appropriate use in business, military, governmental, and academic venues. Essentially, data mining is taking as much information as possible for a variety of databases, sifting it intelligently and coming up with usable information that will help with data prediction, customer service, what if scenarios, and extrapolating trends for population groups (Ye, 2003; Therling, 2009)
When used in CRM it can help in the analysis of the business problem, preparation of data requirements, building of a suitable model to be used in solving a business problem as well as in the validation as well as evaluation of the designed model. Predictive analytics can therefore be used in CRM in order to achieve customer profiling (Ahmed
When used in CRM it can help in the analysis of the business problem, preparation of data requirements, building of a suitable model to be used in solving a business problem as well as in the validation as well as evaluation of the designed model. Predictive analytics can therefore be used in CRM in order to achieve customer profiling (Ahmed, 2004), targeted marketing (Ahmed, 2004), market-based analysis (Meltzer, 2000),management of customer relationships (Edelstein, 2001), fraud detection (Chen et al