TABLE OF CONTENTS The Data Warehouse Quality (DWQ) project is a cooperative project in the ESPRIT program of the European Union whose aim is the establishment of a foundation for data warehouse quality through the linking of semantic models of data warehouse architecture to explicit models of data quality. In this chapter, we present the Data Warehouse Quality project to illustrate one approach for implementing data quality in data warehousing systems.
A data warehouse is a collection of technologies aimed at enabling the knowledge worker (executive, manager, or analyst) to make better and faster decisions. The data warehouse is expected to present the right information in the right place at the right time with the right cost in order to support the right decision. Data warehousing has become an important strategy for integrating heterogeneous information sources in organizations and enabling On-Line Analytic Processing (OLAP). The data warehouse movement is a consequence of the observation by W. Inmon and E.F. Codd, in the early 1990 ?s, that operational-level on-line transaction processing (OLTP) and decision support applications (OLAP) cannot coexist efficiently in the same database environment for two main reasons, both having to do with trade-offs in data quality:
Data characteristics: OLTP databases maintain current data in great detail locally for immediate operational usage, whereas OLAP deals with lightly aggregated and often globally reconciled historical data, covering much more than just the current ones. Mixing both causes complex compromises between different degrees of detail, and varying needs for historical information.
Transaction characteristics:
