Data quality has a substantial impact on the quality of the results of a Knowledge Discovery from Data (KDD) effort. The poor quality of real-world data, as contained in many large data repositories, poses a serious threat to the future adoption of this new technology. Unfortunately, data quality assessment and improvement are often ignored in many KDD efforts, leading to disappointing results. This chapter discusses the use of data mining and data generation techniques, including feature selection, case selection and outlier detection, to assess and improve the quality of the data. In this approach, redundant low quality data are removed from the data repository and new high quality data patterns are dynamically added to the data set. We also point out that data capturing is part of the social practices of office work, and this fact must be taken into account in designing the data capturing processes.