Data is important in quality management extracts, transformations, load processes (ETL), which are fundamental to integration and analysis of data from different sources in data warehouses and analysis. Ensuring high data quality and effective error handling is necessary to maintain the reliability of business intelligence and decision-making processes. This study presents a comprehensive workflow to handle data quality and errors in ETL processes, which address general data quality challenges such as discrepancies, lack of data and duplicate records. The proposed workflow includes a series of structured stages: data profiling, definition of data quality rules, detection of automated errors, improvement mechanisms and ongoing monitoring. These stages are supported by specific techniques such as data, data transformation rules, and vigilant mechanisms for data quality deviations, and ensure rapid detection and dissolution of problems. In addition, the workflow is designed to be adaptable in different data communities, with the provisions on integration into the existing ETL framework. This study is provided to reduce data errors, increase data quality and portray the efficiency of the workflow in ETL process efficiency. This research contributes to the field by offering a standardized approach to managing data quality in ETL processes, promoting better data regime and enabling organizations to make more informed, date-driven decisions.