Enhancing Data Quality Assessment Dashboard for Accuracy and Completeness Dimensions

In the landscape of contemporary data-driven decision-making, the paramount importance of data quality cannot be overstated. This paper introduces a transformative asset in this domain, a Data Quality Assessment Dashboard (DQAD). The DQAD serves as a guiding light, ensuring the integrity of data and providing stakeholders with real-time insights. However, its development is fraught with challenges, often necessitating a choice between custom development and commercial off-the-shelf solutions, both grappling with scalability issues. To address these challenges, this study advocates for a modular DQAD underpinned by open-source frameworks. This approach, which integrates flexibility with scalability, aligns with the nuanced data quality imperatives of organizations. The modular design, distinctly separates the background process from the user interface. Automated modules compute accuracy and completeness scores, enhancing the efficiency of the DQAD. The proposed DQAD emerges as a comprehensive, domainindependent system with modular design principles, aligning with open-source frameworks. Its unique contribution lies in aggregating completeness and accuracy scores through a webbased interface. This work bridges a notable gap in existing research, presenting a holistic DQAD system that caters to diverse domains and provides a foundation for future advancements in the realm of data quality assessment.