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In the era of data-driven decision-making, Data Warehousing (DWH) is crucial for organizations seeking to leverage extensive datasets. However, the success of DWH initiatives depends on the quality of the enclosed data. Insufficient quality data in Data Warehousing can impact the accuracy of analytical results, leading to misguided decisions and reduced business performance. This paper examines the significance of Data Quality Mechanisms in addressing challenges related to data quality. Data Quality Mechanisms play a crucial role in identifying, rectifying, and preventing data quality issues throughout the data lifecycle. This paper explores fundamental concepts, challenges, and impacts of data quality on business operations. It emphasizes the critical role of robust Data Quality Mechanisms in ensuring the accuracy, completeness, and reliability of data within the Data Warehousing ecosystem. As organizations increasingly recognize data as a strategic asset, it is imperative to implement effective data quality mechanisms to unlock the true potential of data warehouses and derive actionable insights.
Georgiev et al. (Sat,) studied this question.