Data Mining is the process of gathering the information from the data of the business and analyzes the various perspectives to provide insight on the business patterns. Data mining concepts and techniques are followed from the origin of business, but the word data mining was not used in the olden days. Data mining is also called as data discovery or knowledge mining.

Data: Facts, Numbers or text stored in the computer about a business. The Online transaction processing OLTP is used to gather the data.
Information: The analysis, patterns and associations between the various data of the business. The Online Analytical Processing OLAP is used to gather information.
Knowledge: Information could be converted in to knowledge of the business, by plotting historical trends, future business growth with the help of the information from the business data warehouse.

The knowledge about the business in turn is used to increase all the prospective of profits to the business and reduce the various loop holes that create business losses. The process of transforming the huge data and large information from all the business sources into knowledge is called the Data Mining.

Data Mining Example:
A super market’s POS Point of Sale system collect various details like the buying pattern of the customers, sales pattern in each of its store location, fast moving products, the peak day of sales in a week, the peak season of sales in the year etc., These information are transformed into the trends, graphs and patterns to the business analysts. This would be helpful for the supermarket business owner to introduce the right sales offers at the right season on the right products or brands to gain the maximum benefit out of the sales.

Data mining is performed to retrieve associations and sequential patterns on the classified and clustered data in the data warehouse.

Classes: Data can be mined on data that are grouped already. Ex: Data grouped as Customer information or Employee information.

Clusters: Data can be mined to understand the logical relations or consumer preferences. Ex: The frequent purchase of the same brand of product by the customer.
Associations: Data can be mined to identify associations. Ex: Men buy cigars on Thursday tend to buy beers along with cigars for the weekend.

Sequential patterns: Data can be mined to identify the behavioral patterns and trends. Ex: On Saturdays most of the people shop for their groceries.

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