Publisher Summary This chapter is an approach to get raw data into a state that is appropriate for mining purposes. Any miner uses these basic techniques for addressing almost any business problem. Miners need to note preparation techniques when mining data in other domains, such as biomedical data, industrial automation data, telemetry data, geophysical data, time domain data, and so on. Developing mined models requires a hard work. It involves working with data, including making changes and taking actions that depend on the needs of the business problem and the miner's data discovery. Mining data is not magic, and it is not something that computer software will do for any one. Basically, data mining is an organized way of working with data, digging out useful information, and application of that useful information in solving the business problems. Most of the tools that are currently in use were developed from three main areas including statistics, artificial intelligence, and machine learning. Despite apparently different roots, these tools essentially do only one thing that is related to the discovery of a relationship, which more or less maps measurements in one part of a data set to measurements in another linked part of the data set.