What is data quality assurance?
Data cleansing, erasing out-of-date information, and data profiling are all steps in the process known as data quality assurance (DQA), which involves screening and identifying anomalies. This procedure must be followed by organizations to guarantee that their data is current and accurate. Such a process must be followed by businesses in order to produce and maintain high-quality data. Due to the effect of persons and outside variables, data are always at risk of being skewed. Organizations can protect the value of their data by implementing a DQA policy across the entire organization. Technical interventions and corporate governance measures are included in this kind of plan. Additionally, thanks to better ML or machine learning models, it enables managers to make more responsible judgments. It is better due to the improved accuracy of Machine Learning models, organizations are also able to make better business decisions as a result of data quality assurance. Better business opportunity identification is made possible by better data quality. Additionally, improved data quality provides businesses with a greater hold on the market.