Data Quality Control in Periodic Reporting Databases

Abstract

Data quality control is an important practice in periodic reporting databases where accurate, complete, and consistent data is required for monthly, quarterly, or annual business reports. In enterprise environments, poor data quality can lead to incorrect summaries, unreliable indicators, delayed reporting, audit issues, and weak decision-making. This article discusses how structured data quality control helps verify data accuracy, completeness, consistency, timeliness, duplication, format validity, and reconciliation status before reports are generated. It explains the role of validation rules, source-to-report mapping, exception logs, data cleansing, approval checks, control totals, and audit trails in improving reporting reliability. The article also highlights common challenges such as missing records, inconsistent coding, late source updates, manual corrections, duplicate entries, and weak ownership of reporting data. A structured data quality control approach is presented to improve report accuracy, reduce correction effort, support compliance, and strengthen confidence in periodic reporting outputs. The study concludes that effective data quality control improves database reliability, supports better business analysis, and ensures dependable reporting in enterprise information systems.