It is estimated that as much as 75% of the effort spent on building a data warehouse can be attributed to back-end issues, such as readying the data and transporting it into the data warehouse. Data quality tools are becoming an increasingly important resource in preparing the data for the warehouse, thus enhancing the usability of the warehouse. This tutorial, based on current research in the field, will focus on a methodology for managing data quality issues. The tutorial will present a framework for identifying data quality issues and making sense of the data quality tools marketplace. A case study approach will be used. The methodology presented is applicable both as a tool to teach about data quality issues and as a tool to support practitioners as they seek mechanisms to facilitate the management of data, yet ensure appropriate data quality.