A framework for quality evaluation in data integration systems

Ensuring and maximizing the quality and integrity of information is a crucial process for today enterprise information systems (EIS). It requires a clear understanding of the interdependencies between the dimensions characterizing quality of data (QoD), quality of conceptual data model (QoM) of the database, keystone of the EIS, and quality of data management and integration processes (QoP). The improvement of one quality dimension (such as data accuracy or model expressiveness) may have negative consequences on other quality dimensions (e.g., freshness or completeness of data). In this paper we briefly present a framework, called QUADRIS, relevant for adopting a quality improvement strategy on one or many dimensions of QoD or QoM with considering the collateral effects on the other interdependent quality dimensions. We also present the scenarios of our ongoing validations on a CRM EIS.