Data Quality Issues

Data Quality Issues refer to problems or inconsistencies in data that affect its accuracy, completeness, consistency, and reliability. These issues can arise from various sources such as human error, system errors, data entry errors, data integration errors, and data storage errors. Poor data quality can lead to incorrect analysis, inaccurate decision-making, and ultimately, negative business outcomes. Data quality issues can be identified through data profiling, data cleansing, and data validation techniques. Data profiling involves analyzing data to understand its structure, content, and relationships. Data cleansing involves correcting or removing errors, inconsistencies, and duplicates in the data. Data validation involves checking the accuracy and completeness of the data against predefined rules or standards.
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