In simple words, data mining is defined as a process used to extract usable data from a larger set of any raw data. It implies analysing data patterns in large batches of data using one or more software. Data mining has applications in multiple fields, like science and research. As an application of data mining, businesses can learn more about their customers and develop more effective strategies related to various business functions and in turn leverage resources in a more optimal and insightful manner. This helps businesses be closer to their objective and make better decisions. Data mining involves effective data collection and warehousing as well as computer processing. For segmenting the data and evaluating the probability of future events, data mining uses sophisticated mathematical algorithms. Data mining is also known as Knowledge Discovery in Data (KDD).
 Key features of data mining:

• Automatic pattern predictions based on trend and behaviour analysis.

• Prediction based on likely outcomes.

• Creation of decision-oriented information.

• Focus on large data sets and databases for analysis.

• Clustering based on finding and visually documented groups of facts not previously known.

The Data Mining Process: Technological Infrastructure Required: 1. Database Size: For creating a more powerful system more data is required to processed and maintained. 2. Query complexity: For querying or processing more complex queries and the greater the number of queries, the more powerful system is required. Uses: 1. Data mining techniques are useful in many research projects, including mathematics, cybernetics, genetics and marketing. 2. With data mining, a retailer could manage and use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. The retailer could also develop products and promotions to appeal to specific customer segments based on mining demographic data from comment or warranty cards.