  Traditional data quality control methods are based on users experience or
previously established business rules, and this limits performance in addition
to being a very time consuming process with lower than desirable accuracy.
Utilizing deep learning, we can leverage computing resources and advanced
techniques to overcome these challenges and provide greater value to users. In
this paper, we, the authors, first review relevant works and discuss machine
learning techniques, tools, and statistical quality models. Second, we offer a
creative data quality framework based on deep learning and statistical model
algorithm for identifying data quality. Third, we use data involving salary
levels from an open dataset published by the state of Arkansas to demonstrate
how to identify outlier data and how to improve data quality via deep learning.
Finally, we discuss future work.
