AutoTest: Automation to Test Tabular Data Quality

Abstract—Data can take various forms and types such as
numbers, symbols, words, images, and graphics. All these
data are significant resources in companies’ operating
process. So, if data has any quality problems (e.g. poor
schema design, data entry errors, misspelling, inconsistency,
and etc.), it will very risky for companies. On the other
hand, high quality data can increase opportunities
dependent on performance issues. This study introduces an
automation tool enabling data quality tests of data
warehouse applications. Also with this tool, historical
changes of a dataset are analyzed with linear regression
algorithm and so outlier variables of data trend are
reported to application user. An experimental results show
the efficiency of proposed automation tool.



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