A Fuzzy Logic-Based Data Integrity Assessment for Data Warehouses

Data warehouses integrate data from multiple sources to support analytical and decision-making processes, making data integrity an important factor in ensuring the reliability of analytical results. Conventional data integrity assessment methods often rely on predefined thresholds or binary classifications, such as valid or invalid, which may not adequately represent the gradual and uncertain nature of data quality. This study proposes a Fuzzy Logic-Based Data Integrity Assessment for Data Warehouses that evaluates data integrity using multiple quality indicators, including accuracy, completeness, consistency, timeliness, and validity. Fuzzy logic is used to transform these indicators into linguistic variables such as Low, Medium, and High and combine them to generate an overall data integrity score ranging from 0 to 1. The proposed approach can be applied during or after the ETL/ELT process to identify potentially unreliable datasets and prioritize them for further investigation or corrective action. By representing data quality as degrees rather than binary classification, the approach aims to provide a more flexible, granular, and interpretable assessment of data integrity in data warehouse environments.