AI Driven Data Quality and Governance Framework for Sap BW/4hana and Sap Business Objects in Multi Cloud AWS Environments
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V8I1P104Keywords:
Data Quality, Data Governance, Artificial Intelligence, SAP BW/4HANA, SAP BusinessObjects, Amazon Web Services, Multi-cloud Architecture, Metadata Management, Predictive Data ValidationAbstract
Contemporary businesses within multi-cloud environments involving the use of SAP BW/4HANA and SAP Business Objects experience ongoing inconsistent data quality, fragmented governance, and underdeveloped real-time exceptions. These are due to the inability of classic rule-based validation and manual stewardship model to scale due to an increase in data volumes and the integration of hybrid clouds, which lead to reporting errors, compliance threats and slack decision-making. The current governance frameworks are mostly reactive even with the improvements in the AI-driven analytics and are metadata-isolated and based on the working SAP flows of data. The proposed area of research lacks scientific understanding of anomaly detection via machine learning, predictive quality scoring, and automated policy enforcement that could be integrated in SAP BW/4HANA architectures applied in a multi-cloud setup. The following paper will present a proposal of data quality and governance framework based on AI, which integrates predictive validation engines, policy orchestration that is aware of metadata, and automatically supported root cause into SAP data pipelines. Its approach is a mixture of data profiling, supervised and unsupervised learning models, and governance rule mining, which is deployed on scalable cloud-native approaches on AWS. The experimental analysis indicates a 37 percent decrease in data errors, 42 percent accuracy improvement of anomaly detection, and 55 percent less work on manual governance than on traditional rule-based systems. The proposed architecture delivers (i) smart data quality judging model, (ii) metadata-driven governance control plane, and (iii) scalable multi-cloud implementation map of enterprise SAP environment.
References
[1] Bayram, F., Ahmed, B. S., Hallin, E., & Engman, A. (2023, June). DQSOps: Data quality scoring operations framework for data-driven applications. In Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering (pp. 32-41).
[2] Guillen-Aguinaga, M., Aguinaga-Ontoso, E., Guillen-Aguinaga, L., Guillen-Grima, F., & Aguinaga-Ontoso, I. (2025). Data quality in the age of AI: A review of governance, ethics, and the FAIR principles. Data, 10(12), 201.
[3] Tewari, S. (2025). AI powered data governance-ensuring data quality and compliance in the era of Big Data. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 8(1), 187-197.
[4] Das, S. S. (2025). Intelligent Data Quality Framework Powered by AI for Reliable, Informed Business Decisions. Journal of Informatics Education and Research, 5(2), 4748-4754.
[5] Davenport, J. M. (2025). Ai-Augmented Frameworks For Data Quality Validation: Integrating Rule-Based Engines, Semantic Deduplication, And Governance Tools For Robust Large-Scale Data Pipelines. International Journal of Advanced Artificial Intelligence Research, 2(08), 9-15.
[6] Kavala, Y. (2023). Smart ERP: Scalable Data Engineering Frameworks Using Artificial Intelligence. International Journal of Computational Mathematical Ideas (IJCMI), 15(1), 1248-1262.
[7] Transforming Enterprise Data Strategy with SAP Business Data Cloud, SAP. online. https://architecture.learning.sap.com/docs/ref-arch/f5b6b597a6
[8] Sanka, V. (2025). Integrating Artificial Intelligence into Enterprise Data Governance Frameworks: A Comprehensive Approach for Automated Compliance and Risk Management. International Journal of Scientific Research in Science, Engineering and Technology, 12(1), 337-345.
[9] Bangad, N., Jayaram, V., Krishnappa, M. S., Banarse, A. R., Bidkar, D. M., Nagpal, A., & Parlapalli, V. (2024). A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments. arXiv preprint arXiv:2410.08576.
[10] Jonnalagadda, R. R., Reddy, K. K., Gunupati, K., Kumar, M., Reddy, P. R. R., & Julakanti, R. (2025, September). Development of an SAP-Centric AI Architecture for Predictive Analytics and Business Intelligence Using Advanced Analytics and AI-Powered Algorithms. In 2025 International Conference on Computing and Communications (COMPUTINGCON) (pp. 1-6). IEEE.
[11] Wilkins, M. (2019). Learning Amazon Web Services (AWS): A hands-on guide to the fundamentals of AWS Cloud. Addison-Wesley Professional.
[12] Lawal, S. (2024). Integrating Artificial Intelligence into SAP Cloud for Intelligent Business Process Automation. Available at SSRN 5386162.
[13] Oviedo, J., Rodriguez, M., Trenta, A., Cannas, D., Natale, D., & Piattini, M. (2024). ISO/IEC quality standards for AI engineering. Computer Science Review, 54, 100681.
[14] Gualo, F., Caballero, I., Rodríguez, M., & Piattini, M. (2023). A data quality model for master data repositories. Informatica, 34(4), 795-824.
[15] Serrano, J. Y., & Zorrilla, M. (2021). A data governance framework for industry 4.0. IEEE Latin America Transactions, 19(12), 2130-2138.
[16] Marcucci, S., Alarcon, N. G., Verhulst, S. G., & Wullhorst, E. (2023). Mapping and Comparing Data Governance Frameworks: A benchmarking exercise to inform global data governance deliberations. arXiv preprint arXiv:2302.13731.
[17] Bögelsack, A., Chakraborty, U., Kumar, D., Rank, J., Tischbierek, J., & Wolz, E. (2022). SAP S/4HANA Systems in Hyperscaler Clouds. Springer. https://link. springer. com/content/pdf/10.1007/978-1-4842-8158-1. pdf.
[18] CHETAN, S., & ADARSH, V. (2022). Converging SAP, AI, and data analytic for transformative business management. WORLD, 14(3), 736-761.
[19] Prasad, M. S. (2025). A Comparative Study of Snowflake and SAP BW for Data Analytics. International Journal of Technology, Management and Humanities, 11(02), 1-8.
[20] Merseedi, K. J., & Zeebaree, S. R. (2024). The cloud architectures for distributed multi-cloud computing: a review of hybrid and federated cloud environment. The Indonesian Journal of Computer Science, 13(2).
[21] Shon, T., & Moon, J. (2007). A hybrid machine learning approach to network anomaly detection. Information Sciences, 177(18), 3799-3821.
[22] Zafar, A. (2024). Balancing the scale: navigating ethical and practical challenges of artificial intelligence (AI) integration in legal practices. Discover Artificial Intelligence, 4(1), 27.
[23] Sebastian-Coleman, L. (2012). Measuring data quality for ongoing improvement: a data quality assessment framework. Newnes.
[24] Cichy, C., & Rass, S. (2019). An overview of data quality frameworks. IEEE Access, 7, 24634-24648.
[25] Foorthuis, R. (2021). On the nature and types of anomalies: a review of deviations in data. International journal of data science and analytics, 12(4), 297-331.
