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As the scale and diversity of data grows in the digital arena, the complexities of data driven engineering grow multifold with it. The last several years have brought forth several new technologies to service this need - semantic Web, grid systems, Web service composition to mention a few. However, a fundamental underpinning of the success of these technologies resides in the quality of data that they can provide. Often the failure of a technology is attributed to its functionality when the real problem lies in the quality of data it uses and subsequently produces. In this paper, we highlight a need to embrace data quality considerations in all aspects of data driven engineering.