Data preprocessing involves cleaning data by handling missing values, outliers, and noise. It also includes integrating and transforming data from multiple sources through normalization, aggregation, and dimensionality reduction. The goals of preprocessing are to improve data quality, handle inconsistencies, and reduce data volume for analysis while retaining essential information. Techniques include discretization, concept hierarchy generation, sampling, clustering, and developing histograms to obtain a reduced data representation.