Criteria B- portfolio 3 fixed

Criteria B:
The IT background of the issue

Data mining is extracted and carried onto the databases a “warehouse system”; the data is loaded and transferred to the main program by wires that are in charge of modifying and manipulating the data. This software is capable of analyzing the data received and classify it into different categories that are determined by the similar and different characteristics within the data. After the data is classified into different groups it has to be transformed so people are able to read it. The data is visualized by using some graphic programs. The technology that is being used is more sophisticated than it used to be few years ago; the application is faster due to the sizes of the databases. More data is being shared and the faster the systems the more data can be shared. The data is going to be transferred faster because better applications and faster processors are going to be created making more data to be shared in one web application and allow the people to contribute to the different categories of data being shared.
There are some vendors of this software such as SAS and IBM (Stacy Cowley, 2005). The next graph will show a general overview of how data mining works

http://www.dmreview.com/media/editorial/dmreview/200001/200001_042_3.gif

The US doctors object to data mining because the web application can share large scales of information and companies are being able to read and analyze the doctor’s prescriptions leading to pressure from the companies to the doctors in order to make them to use or to prescribe more medicine from a certain company. Data mining allows this because it provides a huge range of data bases joined into one application, the software is capable of analyze similarities and difference between data bases and then is able to classify the data according to the relationships presented with it. (Anderson). There are four types of categories in which data is being classified; for example: classes: which places data in destined groups; clusters: the data is categorized as “logical relationships or consumer preferences” which is a clear example of the issue that is being analyzed in this portfolio; within this group the data is identified by different companies or “market segment or consumer affinities”.