Data Quality Assessment in Enterprise Information Systems

Abstract

Enterprise information systems require accurate, complete, consistent, and timely data to support business operations, reporting, compliance, and managerial decision-making. Data quality assessment provides a systematic method for identifying errors, inconsistencies, missing values, duplication, and reliability issues across integrated enterprise databases. Existing literature highlights accuracy, completeness, consistency, validity, timeliness, uniqueness, and integrity as major dimensions for evaluating data quality. However, many organizations still face challenges such as fragmented data sources, weak validation rules, inconsistent master data, duplicate records, outdated information, and limited monitoring of data quality across departments. This research is important because poor data quality can reduce operational efficiency, weaken analytical outputs, and lead to incorrect business decisions. This article discusses data quality assessment in enterprise information systems, focusing on data profiling, validation rules, completeness checks, duplicate detection, consistency testing, integrity verification, and quality monitoring frameworks. The study concludes that effective data quality assessment improves system reliability, strengthens enterprise reporting, reduces data-related risks, and supports more accurate decision-making.