Increasingly, large organizations are engaging in data warehousing projects in order to achieve a competitive advantage through the exploration of the information as contained therein. It is therefore paramount to ensure that the data warehouse includes high quality data. However, practitioners agree that the improvement of the quality of data in an organization is a daunting task. This is especially evident in data warehousing projects, which are often initiated “after the fact”. The slightest suspicion of poor quality data often hinders managers from reaching decisions, when they waste hours in discussions to determine what portion of the data should be trusted. Augmenting data warehousing with data mining methods offers a mechanism to explore these vast repositories, enabling decision makers to assess the quality of their data and to unlock a wealth of new knowledge. These methods can be effectively used with inconsistent, noisy and incomplete data that are commonplace in data warehouses.