Some transformations are dictated by the definition of the business problem. For example, you might want to build a model to predict high-revenue customers. Since your revenue data for current customers is in dollars you need to define what "high-revenue" means. Using some formula that you have developed from past experience, you might recode the revenue attribute into ranges Low, Medium, and High before building the model.
Another common business transformation is the conversion of date information into elapsed time. For example, date of birth might be converted to age.
Domain knowledge can be very important in deciding how to prepare the data. For example, some algorithms might produce unreliable results if the data contains values that fall far outside of the normal range. In some cases, these values represent errors or abnormalities. In others, they provide meaningful information. (See "Outlier Treatment".)