If we are data mining we need a way to verify which data sets are accurate and believe it or not the last set of data may not be the most accurate therefore we cannot simply discard the old data for the new data you see. A paradigm instance of data mining is its use in a retail sales department where a store tracks the purchases of a customer who buys ample of cotton trousers. The data mining system will make an association with customer and cotton trousers and might either directly market or sale the cotton trouser to that customer or try to get the customer to buy a vast range of products. There are several kinds of data mining: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining. The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems. Data mining also enables automatic detection of the patterns in a database and guides marketing professionals to a better understanding of the customer psychology. Data mining primarily consists of using statistical tools and mathematical algorithms. There are many tutorials for understanding the basics of data mining as well as its applications in business. Data mining software enables users to analyze large databases to provide solutions to business decision problems.