Data mining is incorporated for gaining in depth patterns for market intelligence from data warehouses containing massive amount of data. However, the issue that arises is not the quantity of data, as we already have massive amount of data to work with, it is the methodology that is required to learn data. Likewise, data provides all the attributes but how to utilize them for gaining benefit is another question. This is the area that is addressed by data mining, as it is used for extracting valuable information from large amount of data saved on periodic basis. Likewise, information that can be extracted may contain relationships and different patterns. For instance, a retail store may indicate that some products are more in demand in one channel of distribution, there may be two different products that are sold at the same time in a specific geographic location, some specific products are more in demand in some geographic locations and similarly, some products are more demanding in certain events may be associated with religious events. If we take an example of Wal-Mart, the store has found that if there is a probability of a hurricane, the demand of beet increases in that specific geographical area, therefore, stores have to stock more beers that usual in this sort of situation (Keating 2008). Employee associated with utilizing patterns of customer behavior from data mining, i.e. a financial analyst would seek facets of the store or organization that may become bankrupt, similarly, human resource managers would seek information of a successful potential employee, employees working in a credit card department would like to get information associated with credit card debts payments from potential customers and also to analyze the legitimate credit card transactions against the falsified ones, marketing department executives would like to extract information associated with product