Unified Data Fabrics for Multi-Sector AI Forecasting
Keywords:
Big Data Architectures,Artificial Intelligence,Predictive Analytics,Data Engineering,Machine Learning Models,Cloud Computing Platforms,Data Lakes and Warehousing,Real-Time Data Processing,Digital Ecosystems,Multi-Industry Integration,Scalable Data Infrastructure,Edge Computing,Data Governance,Intelligent Decision Systems,Advanced Analytics.Abstract
This study assembles and evaluates intelligent big-data architectures that support AI-driven predictive analytics across multiple domains, using a common use-case structure in which predictive models are trained in one domain and evaluated in others. This approach surfaces architectural characteristics and patterns that improve the quality and appeal of predictive-analytics solutions within multi-industry digital ecosystems. A proposed predictive-analytics maturity model provides a roadmap for aligning data strategy, technological capability, and organizational readiness and investment to build more intelligent architectures and deliver more successful predictive-analytics outcomes. Key contributors to predictive-analytics-ready architectures include attention to data quality, governance, and lineage; well-designed feature stores; robust ingestion, storage, and management of data pipelines; and support for diverse data consumers—modelers, business-expert validators, and domain-specific data scientists and engineers. Together, these elements enable architectures capable of exploiting cross-domain transfer learning for both training and evaluation.
References
1. Alsharef, A., Aggarwal, K., Sonia, Mishra, A., & Kumar, M. (2022). Review of ML and AutoML solutions to forecast time-series data. Archives of Computational Methods in Engineering, 29, 5297–5311.
2. Araújo Machado, I., Costa, C., & Santos, M. Y. (2022). Data mesh: Concepts and principles of a paradigm shift in data architectures. Procedia Computer Science, 196, 263–271.
3. Sukumar, S., Kumar, S., Yandamuri, U. S., & KV, M. N. (2026). Business Ethics and Corporate Sustainability. BR Publications.
4. Blohm, I., Wortmann, F., Legner, C., & Köbler, F. (2024). Data products, data mesh, and data fabric: New paradigm(s) for data and analytics? Business & Information Systems Engineering, 66, 643–652.
5. Donta, P. K., Dehury, C. K., & Hu, Y.-C. (2024). Learning-driven data fabric trends and challenges for cloud-to-thing continuum. Journal of King Saud University Computer and Information Sciences, 36, Article 102145.
6. Gieß, A., & Hutterer, A. (2025). The future of data management: A delimitation of data platforms, data spaces, data meshes, and data fabrics. Information Systems and e-Business Management, 23, 971–997.
7. Sharma, C. K., Gorle, R., Mangala, N., Jindal, A., J, A., & Sundram, S. (2026). Blockchain-Based Human Resource Management System for Transparent Workforce Governance. In 2026 7th International Conference On Computational Vision and Bio Inspired Computing (ICCVBIC) (pp. 1741–1745). IEEE. 2026 7th International Conference On Computational Vision and Bio Inspired Computing (ICCVBIC). https://doi.org/10.1109/iccvbic71195.2026.11689175
8. Gu, Z., Corcoglioniti, F., Lanti, D., Mosca, A., Xiao, G., Xiong, J., Calvanese, D., & others. (2024). A systematic overview of data federation systems. Semantic Web, 15(1), 107–165.
9. Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), Article 71.
10. Kanagarla, K. P. B. (2024). Data fabric: A new approach to data integration. International Journal of Innovative Engineering and Management Research, 13(10).
11. Penaka, B. (2026). AUTONOMOUS MULTI-AGENT AI FRAMEWORK FOR END-TO-END CLINICAL DECISION SUPPORT AND CARE COORDINATION. Genetics and Molecular Research.
12. Kim, J., Kim, H., Kim, H., Lee, D., & Yoon, S. (2025). A comprehensive survey of deep learning for time series forecasting: Architectural diversity and open challenges. Artificial Intelligence Review, 58, Article 216.
13. Kong, X., Chen, Z., Liu, W., Ning, K., Zhang, L., Marier, S. M., Liu, Y., Chen, Y., & others. (2025). Deep learning for time series forecasting: A survey. International Journal of Machine Learning and Cybernetics, 16, 5079–5112.
14. Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11, 31866–31879.
15. Kuftinova, N. G., Ostroukh, A. V., Filippova, N. A., Gaevskii, V. V., & Podgorny, V. V. (2022). Integration of scalable IT architectures on the basis of data fabric technology. Russian Engineering Research, 42(11), 1199–1202.
16. Kuftinova, N. G., Maksimychev, O. I., Ostroukh, A. V., Volosova, A. A., & Matukhina, E. N. (2022). Data fabric as an effective method of data management in traffic and road systems. In 2022 Systems of Signals Generating and Processing in the Field of Onboard Communications (pp. 1–4). IEEE.
17. Arunakumari, M., Mattaparthi, R., Kothuri, S., Prakash, V. D., & Prasad, E. S. (2026, July). Data-Driven Framework for Real-Time Carbon Accounting and Sustainability Monitoring in Off-Grid Urban Residential Microgrids. In 2026 14th International Conference on Smart Grid (icSmartGrid) (pp. 493-500). IEEE.
18. Legner, C., Pentek, T., & Otto, B. (2020). Accumulating design knowledge with reference models: Insights from 12 years’ research into data management. Journal of the Association for Information Systems, 21(3).
19. Li, W., & Law, K. L. E. (2024). Deep learning models for time series forecasting: A review. IEEE Access, 12, 92306–92327.
20. Bandi, V. D. V. K. Autonomous Data Platforms: Converging AI, MLOps, and Cloud Engineering for Digital.
21. Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194), Article 20200209.
22. Liu, K., Yang, M., Li, X., Zhang, K., Xia, X., & Yan, H. (2022). M-data-fabric: A data fabric system based on metadata. In 2022 5th International Conference on Big Data and Artificial Intelligence (pp. 57–62). IEEE.
23. Macías, A., Muñoz, D., Navarro, E., & González, P. (2024). Data fabric and digital twins: An integrated approach for data fusion design and evaluation of pervasive systems. Information Fusion, 103, Article 102139.
24. Potturi, P. (2026). Agentic Business Intelligence Architecture for Autonomous Data Exploration, Insight Generation, and Strategic Decision Orchestration. International Journal of Computer Information Systems and Industrial Management Applications, 18(22s), 536-542.
25. Rieyan, S. A., News, M. R. K., Rahman, A. B. M. M., Khan, S. A., Zaarif, S. T. J., Alam, M. G. R., Hassan, M. M., Ianni, M., & Fortino, G. (2024). An advanced data fabric architecture leveraging homomorphic encryption and federated learning. Information Fusion, 102, Article 102004.
26. Song, X., Deng, L., Wang, H., Zhang, Y., He, Y., & others. (2025). Deep learning-based time series forecasting. Artificial Intelligence Review, 58, Article 23.
27. Mahadevan, S. (2026). Governed Serverless Automation Ecosystem for AI-Driven Enterprise Integration and Sustainable Cloud Operations. International Journal of Computer Information Systems and Industrial Management Applications, 18(16s), 1561-1570.
28. Su, L., Zuo, X., Li, R., Wang, X., Zhao, H., & others. (2025). A systematic review for transformer-based long-term series forecasting. Artificial Intelligence Review, 58, Article 80.
29. Hall, T., & Rasheed, K. (2025). A survey of machine learning methods for time series prediction. Applied Sciences, 15(11), Article 5957.
30. Abu Rumman, A., & Al-Abbadi, L. (2023). Structural equation modeling for impact of data fabric framework on business decision-making and risk management. Cogent Business & Management, 10(2), Article 2215060.
31. Bode, J., Kühl, N., Kreuzberger, D., & Hirschl, S. (2023). Data mesh: Motivational factors, challenges, and best practices. arXiv preprint.
32. Movva, V. R., & Pareek, P. K. (2026). Multi-Agent AI Architecture for Autonomous Retail Operations: Coordinating Intelligent Agents Across Cloud, DevOps, and Enterprise Platform. International Journal of Computer Information Systems and Industrial Management Applications, 18(23s), 925-932.
33. Wider, A., Verma, S., & Akhtar, A. (2023). Decentralized data governance as part of a data mesh platform: Concepts and approaches. In 2023 IEEE International Conference on Web Services.
34. Ye, H., Zhang, N., Chen, H., & Chen, H. (2022). Generative knowledge graph construction: A review. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
35. Alsharef, A., Aggarwal, K., Sonia, Mishra, A., & Kumar, M. (2022). Review of machine learning and automated machine learning solutions for time-series forecasting. Archives of Computational Methods in Engineering, 29, 5297–5311.
36. Song, H., Rajan, D., Thiagarajan, J. J., & Spanias, A. (2020). Attend and diagnose: Clinical time series analysis using attention models. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 4091–4098.
37. Loganathan, R. (2026). SaaS Entitlement Governance: A Reproducible Model for Detecting and Reclaiming Over-Provisioned Access at Enterprise Scale. Journal of Intelligent Decision Making and Information Science, 3(7s), 2519-2530.
38. Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021). Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748–1764.
39. Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2023). Transformers in time series: A survey. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (pp. 6778–6786).
Additional Files
Published
Data Availability Statement
No new data were created or analyzed in this study. Data sharing is not applicable to this article.
Issue
Section
License
Copyright (c) 2026 Jai Kiran Reddy Burugulla, Thomas Müller (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
