| There are different kinds of solutions out in the research community for improving data quality. Most of them are already in use in the software industry but some are specific for the application they are made for because of the inherited challenges. A detailed review of about 200 carefully selected papers was conducted and the information obtained is divided into challenges and solutions. We were able to identify three major research gap that are as follows 1) looking at Data Quality with full enterprise's picture in mind 2) no one talked about modelling for Human-Independent Human Experience Retention (Hi-HER) on Data Errors and 3) no one discussed the possibility of converting Error Patterns into SQL Statements for perpetual execution for enterprise-wide data quality protocol compliance. Using the proposed algorithm and system, every time a data entry error is detected a corresponding SQL statement is generated in a way that if the same data entry error/fraud (Double-Dipping) occurs again, the system would be able to capture those erroneous records using the same SQL statement. The tested SQL statement is then fed to the system along with the related correction agent account/email address. Over time, the system will have collected all the error patterns in SQL statements format and related email addresses of correction agents for automatic error detection and eradication.;Keywords: Data Errors Reduction, Data Fraud Detection, Data Quality Improvement, Double Dipping in Medicare and Medicaid, Error Pattern in SQL Statement, Human Independent Human Experience Retention (Hi-HER) modelling, Scanning RDBMS for Degree of Compliance. |
