Resumen
To increase productivity, organizations must manage information as they manage products. The field of information quality (IQ) has experienced significant advances during its relatively brief history. Today, researchers and practitioners alike have moved beyond establishing information quality as an important field to resolving IQ problems ranging from IQ definition, measurement, analysis, and improvement to tools, methods, and processes. However, theoretically-grounded methodologies for Total Data Quality Management (TDQM) are still lacking. Based on cumulated research efforts, this article presents such a methodology for addressing these problems. The purpose of this TDQM methodology is to deliver high quality information products to information consumers. It aims to facilitate the implementation of an organization's overall data quality policy formally expressed by top management. Organizations of the 21st century must harness the full potential of their data in order to gain competitive advantage and attain strategic goals.

Resumen
Data-quality (DQ) problems are increasingly evident, particularly in organizational databases. Organizational databases, however, reside in the larger context of information systems (IS). Within this larger context, data is collected from multiple data sources and stored in databases. DQ problems may arise anywhere in this larger IS context. With few exceptions, DQ is treated as an intrinsic concept, independent of the context in which the data is produced and used. This focus on intrinsic DQ problems in stored data fails to solve complex organizational problems. Mismatches among sources of the same data are a common cause of intrinsic DQ concerns. Initially, data consumers do not know the source to which quality problems should be attributed; they know only that data is conflicting. Thus, these concerns initially appear as "believability" problems. Over time, information about the causes of mismatches accumulates from evaluations of the accuracy of different sources, which lead to poor reputation for less accurate resources.