With decades of research on the various aspects of data cleaning, multiple technical challenges have been tackled and interesting results have been published in many research papers. Example quality problems include missing values, functional dependency violations and duplicate records. Unfortunately, very little success can be claimed in adopting any of these results in practice. Businesses and enterprises are building silos of home-grown data curation solutions under various names, often referred to as ETL layers in the business intelligence stack. The impedance mismatch between the challenges faced in industry and the challenges tackled in research papers explain to a large extent the growing gap between the two worlds. In this talk I claim that being pragmatic in developing data cleaning solution does not necessarily mean being unprincipled or ad-hoc. I discuss a subset of these practical challenges including data ownership, human involvement, and holistic data quality concerns. These new set of challenges often hinder current research proposals from being adopted in the real world. I also go through a quick overview of the approach we use in tamr (a data curation startup) to tackle these challenges.