The "Interactive Data Quality Dashboard" integrates real-time monitoring with predictive analytics to enhance proactive data management and support high standards of data governance. In response to the exponential growth in data generation across modern organizations, this dashboard provides a critical solution for maintaining data quality, integrity, and consistency. Leveraging predictive analytics, the system forecasts potential data quality challenges, allowing users to address issues before they escalate. By enabling early detection of data inconsistencies, this platform fosters a preventative approach to data management that significantly reduces risks associated with data discrepancies. Designed with user-friendliness in mind, the dashboard provides intuitive interfaces and real-time feedback mechanisms that simplify the visualization, assessment, and management of data quality. Users are equipped with actionable insights that support continuous improvement in data accuracy, completeness, and consistency across various data environments. Additionally, the automation of data validation processes minimizes manual effort, streamlining workflows and increasing operational efficiency. This proactive approach not only enhances decision-making capabilities but also supports strategic data-driven initiatives within organizations. By continuously analyzing and visualizing real-time data quality metrics, the dashboard ensures that data remains reliable and ready for effective use. The integration of predictive algorithms allows organizations to adapt to emerging trends and address future data challenges, fostering resilience and adaptability in data management practices. For organizations aiming to uphold high standards in data governance and quality control, the Interactive Data Quality Dashboard offers a powerful tool that combines advanced analytics with real-time monitoring to drive sustainable data quality management.

Data Quality, Predictive Analytics, Real-Time Monitoring, Data Integrity, Proactive Data Management, Data Validation, Data Consistency, Operational Efficiency

[1] Acquisti, L. Brandimarte, and G. Loewenstein, "Privacy and human behavior in the age of information," Science, Vol.347, No.6221, pp.509-514, 2019.
[2] S. LaValle, E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz, "Big Data, Analytics and the Path from Insights to Value," MIT Sloan Management Review, 52, 2, 2011.
[3] T. H. Davenport and R. Ronanki, "Artificial intelligence for the real world," Harvard Business Review, Vol.96, No.1, pp.108-116, 2018.
[4] L. A. Siiman, et al., "Opportunities and Challenges for AIAssisted Qualitative Data Analysis: An Example from Collaborative Problem-Solving Discourse Data," in International Conference on Innovative Technologies and Learning, Cham: Springer Nature Switzerland, 2023.
[5] Z. Wang, A. Maalla and M. Liang, "Research on E-Commerce Personalized Recommendation System based on Big Data Technology," 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, pp.909-913, 2021.
[6] A. K. Sharma, N. Goel, J. Rajput and M. Bilal, "An Intelligent Model For Predicting the Sales of a Product," 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, pp.341-345, 2020.
[7] V. Geetha, M. C. Manikandan, R. Rejith, R. S. Rishi, and K. Umapathy, "A Survey on E-Commerce Recommendation Systems Using Artificial Intelligence and Current Trends for Personalization to Improve Customer Experience," International Journal of Engineering Research & Technology (IJERT), March, Vol.13, No.3, 2024.
[8] D. Marupaka and S. Rangineni, "Machine Learning-Driven Predictive Data Quality Assessment in ETL Frameworks," International Journal of Computer Trends and Technology, Vol.72, No.3, pp.53-60, 2024.
[9] S. Rangineni and A. K. Bhardwaj, “Analysis Of DevOps Infrastructure Methodology and Functionality of Build Pipelines”, EAI Endorsed Scal Inf Syst, Jan., Vol.11, No.4, 2024.
[10] N. S. A. Polireddi, M. Suryadevara, S. Venkata, S. Rangineni, S. K. R. Koduru and S. Agal, "A Novel Study on Data Science for Data Security and Data Integrity with Enhanced Heuristic Scheduling in Cloud," 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, pp.1862-1868, 2023.
[11] L. W., “Real-time Data Quality Monitoring in Data Warehousing.” Journal of Data Management, Vol.21, Issue.4, 15-29, 2015.
[12] T. Esther, A. Akinsola, T. Ahmed, E. Makinde, K. Akinwande, and M. Mayowa, "A Review of the Ethics of Artificial Intelligence and Its Applications in the United States," International Journal on Cybernetics & Informatics (IJCI), Vol.12, No.6, 2023.
[13] V. Antonio, "How AI is changing sales,"Harvard Business Review, Vol.30, 2018.
[14] W. M. Lim, S. Kumar, N. Pandey, D. Verma, and D. Kumar, "Evolution and trends in consumer behavior: Insights from Journal of Consumer Behavior," Journal of Consumer Behavior, Vol.22, No.1, pp.217-232, 2023.
[15] Y. Gao and H. Liu, "Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective," Journal of Research in Interactive Marketing, Vol.17, No.5, pp.663-680, 2023.
[16] G. S. Gautham and S. Rao, "The Impact of Artificial Intelligence on Personalized Marketing," International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol.12, No.4, 2024.
[17] P. Sinha, A. Shastri, and S. Lorimer, "How generative AI will change sales,"Harvard Business Review, 2023.
[18] Xu, Y., & Zhao, Z., “Predictive Analytics for Proactive Data Quality Management.” International Journal of Information Management, Vol.38, Issue.1, pp.45-56, 2018.
[19] Sweeney, L., “The Role of Predictive Analytics in Enhancing Data Quality.” Data Science Journal, Vol.17, Issue.2, pp.78-92, 2019.
[20] Zhang, J., & Wang, L., “Integrating Real-Time Monitoring with Predictive Analytics for Improved Data Quality.” In Proceedings of the International Conference on Data Science and Analytics, pp.112-120, 2020.