Enterprise Data Harmonization Engine
Keywords:
Discoverability: insight engineering; business intelligence; multi-tier architecture; data harmonization; enterprise data ecosystem; federated analytics; federated learning; data governance; metaphysics; ethics.Abstract
Federated insight engineering offers an industry-agnostic approach to federated analytics by exploiting a multi-tiered architecture for enterprise data within the larger FORGE methodology. The concept is rooted in the view of enterprise data landscapes as a collection of data sources susceptible to local analysis and insight extraction. Data sharing between these sources occurs where partners have a common business interest, and these ephemeral partnerships are governed by collaboration agreements.
Few industries have yet mastered the art of federated insight engineering. However, regulatory constraints and the privacy paradox mean that lessons learned in heavily regulated environments are applicable—often with minor adjustments—to other domains confronting different but equally pressing motivations. Tourism & hospitality, online marketplace, and finance & insurance exemplify the early movers in federated insight engineering within the analytics supply chain. Industry exemplars highlight not only common patterns of success and failure but also a reference architecture for federated analytics at the paradigm level.
References
1. Kokash, N., Wang, L., Gillespie, T. H., Belloum, A., Grosso, P., Quinney, S., Li, L., & de Bono, B. (2025). Ontology- and LLM-based data harmonization for federated learning in healthcare. arXiv.
2. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
3. Stripelis, D., & Ambite, J. L. (2023). Federated learning over harmonized data silos. arXiv.
4. Zhang, F., Kreuter, D., Chen, Y., Dittmer, S., Tull, S., Shadbahr, T., Preller, J., Aston, J. A. D., Schönlieb, C.-B., Gleadall, N., & Roberts, M. (2023). Recent methodological advances in federated learning for healthcare. arXiv.
5. Li, M., Wang, Y., Zhao, X., & Chen, H. (2025). Implementing federated learning in healthcare: Challenges and opportunities. Medical Image Analysis, 92, 103112.
6. Kolla, T. (2024). Graph Neural Networks for HCC Risk Adjustment and Interoperability. International Journal of Science, Research and Technology, 7(6), 13244-13255.
7. Luo, H., & Ji, C. (2025). Federated learning-based data collaboration method for enhancing edge cloud AI system security using large language models. arXiv.
8. Tölle, M., Burger, L., Kelm, H., André, F., Bannas, P., & Engelhardt, S. (2024). Multi-modal dataset creation for federated learning with DICOM structured reports. arXiv.
9. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A., Bonawitz, K., Charles, Z., Cormode, G., & Cummings, R. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
10. Inala, R. (2023). AI-powered investment decision support systems: Building smart data products with embedded governance controls. Journal for ReAttach Therapy and Developmental Diversities, 6(10), 2251-2266.
11. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273–1282.
12. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.
13. Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., & Suresh, A. T. (2020). SCAFFOLD: Stochastic controlled averaging for federated learning. Proceedings of the 37th International Conference on Machine Learning, 5132–5143.
14. Kolla, S. K., & Mangalampalli, B. M. (2024). Edge-Based Deep Learning Systems for Point-of-Care Diagnostic Intelligence. Journal of Neonatal Surgery, 13(1), 2387-2399.
15. Reddi, S. J., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2021). Adaptive federated optimization. International Conference on Learning Representations.
16. Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2020). Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 92–104.
17. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
18. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3(1), 119.
19. Karargyris, A., Umeton, R., Sheller, M. J., Aristizabal, A., & George, J. (2023). Federated benchmarking of medical artificial intelligence with MedPerf. Nature Machine Intelligence, 5(8), 799–810.
20. Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., & Van Overveldt, T. (2019). Towards federated learning at scale: System design. Proceedings of Machine Learning and Systems, 1, 374–388.
21. Kolla, S. K., & Reddy, V. A. R. (2024). Evaluating Cloud-Native vs. Hybrid Architectures for Health Benefit Administration Systems. International Journal of Medical Toxicology and Legal Medicine, 27(5), 1042-1053.
22. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2, 429–450.
23. Hardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, B., & Thorne, B. (2017). Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv.
24. Overman, T., Blum, G., & Klabjan, D. (2024). A primal-dual algorithm for hybrid federated learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(12), 13544–13552.
25. Guo, J., Mu, H., Liu, X., Ren, H., & Han, C. (2025). Federated learning for biometric recognition: A survey. Artificial Intelligence Review, 58(2), 1–38.
26. Eden, R., Chukwudi, I., Bain, C., Barbieri, S., & Callaway, L. (2025). A scoping review of the governance of federated learning in healthcare. NPJ Digital Medicine, 8(1), 144.
27. Gottimukkala, V. R. R. (2024). Federated Learning Approaches for Fraud Detection in International Payment Systems. https://www. jisem-journal. com/download/118_JISEM. pdf.
28. Barbereau, T., Delgado Fernandez, J., & Potenciano Menci, S. (2025). The governance of federated learning: A decision framework for organisational archetypes. Data & Policy, 7, e25.
29. Liu, Y., Zhang, X., Kang, Y., Li, L., Chen, T., Hong, M., & Yang, Q. (2022). FedBCD: A communication-efficient collaborative learning framework for distributed features. IEEE Transactions on Signal Processing, 70, 4277–4290.
30. Zhang, X., Yin, W., Hong, M., & Chen, T. (2020). Hybrid federated learning: Algorithms and implementation. NeurIPS Workshop on Scalability, Privacy, and Security in Federated Learning.
31. Sheth, A., & Larson, J. A. (1990). Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Computing Surveys, 22(3), 183–236.
32. Hedberg, T. D., Bajaj, M., & Camelio, J. A. (2020). Using graphs to link data across the product lifecycle for enabling smart manufacturing digital threads. Journal of Computing and Information Science in Engineering, 20(2), 021008.
33. Inala, R., & Somu, B. (2024). Agentic ai in retail banking: Redefining customer service and financial decision-making. Journal of Artificial Intelligence and Big Data Disciplines, 1(1), 1-19.
34. Zhang, Q., Liu, J., & Chen, X. (2024). A literature review of the digital thread: Definition, key technologies, and applications. Systems, 12(2), 78.
35. Katta, T. B. (2025). Federated learning for enterprise data integration: Examining the application of federated learning to integrate AI models without centralizing enterprise data. Journal of Information Systems Engineering and Management, 10(63s).
36. Na, S., Rouček, T., Ulrich, J., Pikman, J., & Krajník, T. (2022). Federated reinforcement learning for collective navigation of robotic swarms. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1517–1528.
37. Yu, X., Queralta, J. P., & Westerlund, T. (2021). Towards lifelong federated learning in autonomous mobile robots with continuous sim-to-real transfer. Procedia Computer Science, 184, 406–412.
