Rule-based data quality David Loshin   View Paper (PDF)   Return to Industry session 1: knowledge management and semantics Abstract In the business intelligence/data warehouse user community, there is a growing confusion as to the difference between data cleansing and data quality. While many data cleansing products can help in applying data edits to name and address data, or help in transforming data during an ETL process, there is usually no persistence in this cleansing. This paper describes how we have implemented a business rules approach to build a data validation engine, called GuardianIQ, that transforms declarative data quality rules into code that objectively measures and reports levels of data quality based on user expectations. DiSC'03 © 2003 Association for Computing Machinery