Workshop Organizers’ Message

Abstract

Translate Article

Poor data quality is known to compromise the credibility and efficiency of commercial as well as public endeavours. Several developments from industry and academia have contributed significantly towards addressing the problem. These typically include analysts and practitioners who have contributed to the design of strategies and methodologies for data governance; solution architects including software vendors who have contributed towards appropriate system architectures that promote data integration and; and data experts who have contributed to data quality problems such as duplicate detection, identification of outliers, consistency checking and many more through the use of computational techniques. The attainment of true data quality lies at the convergence of the three aspects, namely organizational, architectural and computational.KeywordsData QualityPoor Data QualityProvenance InformationSource AttributionCollaborative SettingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.