Legal Obstacles What are the legal obstacles to data mining? What problems could occur? What solutions would you propose? The legal obstacles to data mining are data ownership, privacy of the data, and the expected results (Jensen, Jensen, & Brunak, 2012)
It is vital that individuals are allowed the freedom to exercise their religion without having to incur heavy burdens due to their business. The mandate placed upon the Greens by the Health and Human Services for them to provide life-terminating drugs in their health insurance was infringing on their constitutional right of religion freedom (Shapiro, 2014)
Which benefits they receive, however, can also depend on the way in which their data mining is undertaken. Predictive analytics are used to understand customer behavior, and businesses use the behavior of the customer in the past to attempt to determine what the customer will do in the future (Cabena, et al
In other words, predictive analytics look at what customer A will buy again, but association discovery looks at what customer A will buy based on his belonging to a particular group of customers who also buy a particular product. Web mining is used to discover information about customers on the web (Hastie, Tibshirani, & Friedman, 2001)
, 1997). While it is not an exact science, many companies believe they can use it in order to decide which products will sell most often to which customers (Nisbet, Elder, & Miner, 2009)
) deploys a case study approach to examine how data mining can be used in clinical support systems to reduce costs in an increasingly cash-strapped healthcare environment. The specific focus was upon unprofitable DRGs [diagnosis-related groups] and attempted to discern the negative financial impact of specific types of Medicaid and Medicare patients upon a facility (Silver et al
in the healthcare industry data mining is increasingly becoming popular if not essential. Data mining applications are beneficial to all parties that are involved in the healthcare industry including care providers, HealthCare organizations, patients, insurers and researchers (Kirby, Flick,
The validation of each branch's identity needs to occur with each login. Distributed database security concepts of interdomain trust relationships and the use of security audits to ensure that role-based access are being monitored are best practices in managing distributed database security (Harris, Sidwell, 1994)
The sales, marketing, product management, product marketing, and services departments all need to have access to the databases and data mining applications. In addition, branch offices that access the company's applications over the shared T1 line will also need to have specific security roles assigned, especially if application and data are being accessed over the Web (Maheshwari, 1999)
For the databases and data mining software, suing biometrics to secure them at the administrator level is highly advisable (Amoruso, Brooks, Riley, 2005). In addition, the defining of IPSec protocols configuration options for the dedicated lines to the branches is advisable (Mattsson, 2009)
All of these factors need to be taken into account in creating an enterprise-wide security strategy. By definition an enterprise-wide security strategy concentrates on the how the systems, processes, and procedures of a company are protected and hardened from external threats so that a company's strategic objectives and plans can be attained (Yang, Li, Deng, Bao, 2010)
One of the most prevalently used segmentation criterion that consumer products companies rely on is demographic variables. These include age, gender, family size, income, occupation, education, religion, race and nationality (Craft, 2001)
Market research is also necessary for mitigating risks of new ventures including the offering entirely new products or services, or extensions to existing product lines. Secondary research can meet the broader information needs of these strategies, yet to gain information and insight specific to the given strategic goals, primary research is often needed (Ganeshasundaram, Henley, 2006)
The use of data mining has become more pervasive in marketing, sales and service as organizations strive to gain insights from the terabytes of data they have accumulated over years and in some cases decades of operation. Data mining can provide marketers with greater insights into the preferences, needs and wants of customers, in addition to potential new product or service ideas based on a careful analysis of the accumulated data on customer bases (Koh, Kin, 2002)
Often these technologies are used to create a single system of record used for analysis and advanced queries by the enterprises who build them. Data mining is often included in business intelligence (BI) suites and the analytics layer of an enterprise-wide computing system, as each application needs to gain access to the metrics and key performance indicators (KPIs) (Peacock, 1998)
What are the approaches to market segmentation? There are four major types of market segmentation used today. The most common are demographic and geographic with behavioral and psychographic being most often used for consumer products (Tuma, Decker, Scholz, 2011)
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). Data mining is the study of how to glean insights and intelligence from data sets which are often not integrated with each other in a common database, further adding a level of abstraction to the analysis, making its interpretation even more difficult (Buddhakulsomsiri, Zakarian, 2009)
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
All also share the need for using the data in their companies for getting an understanding of how strategies in place today will yield results in the future (Kuhn, Ducasse, Girba, 2007). Data mining also requires an intensive level of data integration across databases, legacy and often standalone systems, in addition to a redefining of the most critical processes used for accumulating information in the first place (da Cunha, Agard, Kusiak, 2010)
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)