The current reporting system within the organization is fragmented across multiple platforms, with much of it managed manually. This approach leads to inefficiencies, data inconsistencies, and limited real-time visibility, increasing compliance risks and potential financial losses. A unified Data Quality Reporting Dashboard is needed to consolidate reports, automate updates, and deliver real-time insights, ultimately improving data accuracy and operational efficiency.

A systematic approach was employed, combining qualitative and quantitative methods: Requirements Elicitation: Insights were gathered from stakeholders through interviews and workshops, with all requirements clearly documented using organizational tools. Literature Review: Internal sources and academic papers were analyzed for trends and structure. Data Analysis: Statistical tools were applied to identify patterns, define quality metrics, and ensure accuracy. Prototype Development and Pilot Implementation: Mockups were created, and a prototype was developed using an iterative approach.

The dashboard is designed to streamline reporting processes, improve data accuracy, enhance risk management, and empower decision-making through real-time data insights. To achieve this, data is collected from various sources within the organization’s IT infrastructure and stored in a centralized data warehouse. Automated API connections will periodically load data into this central storage, which then undergoes an ETL (Extract, Transform, Load) process to prepare it for analysis and reporting. The final datasets are sent to the frontend, where they are utilized in various objects. The frontend view is made accessible to end users via a web service. A visual representation of this architecture is available in the illustration at the beginning. The development of the Data Quality Reporting Dashboard marks a significant step forward in optimizing data management and reporting processes. This prototype provides a solid foundation for future improvements, with the aim of maintaining high data quality, enhancing operational efficiency, and supporting strategic decision-making. Future enhancements could include AI for pattern detection and other innovative applications.