Data Quality Evaluation using Probability Models

Abstract

This paper discusses an approach with machine-learning probability models to evaluate the difference between good and bad data quality in a dataset. A decision tree algorithm is used to predict data quality based on no domain knowledge of the datasets under examination. It is shown that for the data examined, the ability to predict the quality of data based on simple good/bad pre-labelled learning examples is accurate, however in general it may not be sufficient for useful production data quality assessment.