Research Statement Data Cleaning Algorithmic Data-cleaning Techniques

With the increasing amount of available data, turning raw data into actionable information is a requirement in every field. However, one bottleneck that impedes the process is data cleaning. Data analysts usually spend over half of their time cleaning data that is dirty — inconsistent, inaccurate, missing, and so on — before they even begin to do any real analysis. It is a time consuming and costly process but necessary for obtaining high-quality answers from dirty data. This is further exacerbated in emerging Big Data scenarios when data volumes are increasing, or when the data is integrated from a larger variety of sources.