A dynamic and user-centric data cleaning system for Watson Analytics

数据质量差是影响所有行业组织的一个严重且代价高昂的问题。实际数据通常是脏的，包含丢失、错误、不完整和重复的值。据估计，糟糕的数据质量会使组织花费其运营预算的15%至25%。现有的数据清理解决方案侧重于识别不符合规定数据格式的不一致，假设数据保持相对静态。随着现代应用程序转向更动态的搜索和分析，需要支持动态和自适应数据清理的新数据质量解决方案。

Poor data quality is a serious and costly problem affecting organizations across all industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate values. It is estimated that poor data quality cost organizations between 15% and 25% of their operating budget. Existing data cleaning solutions focus on identifying inconsistencies that do not conform to prescribed data formats assuming the data remains relatively static. As modern applications move towards more dynamic search and analytics, new data quality solutions that support dynamic and adaptive data cleaning are needed.