Toward High-Fidelity Healthcare Digital Twins: Integrating Real-Time Processing, Data Mesh, and MDM (Published)
Healthcare digital twins are emerging as powerful tools for simulating patient conditions and operational workflows in real time. This paper explores the architectural and technical foundation necessary for building high-fidelity digital twins—those capable of accurate, synchronized, and responsive modeling. It identifies key challenges, including fragmented data, latency, poor semantic alignment, and identity inconsistencies. To overcome these, the study proposes a five-layer architecture integrating real-time data processing, data mesh principles, and master data management (MDM). Through case studies involving heart failure monitoring and hospital operations, the research demonstrates improvements in fidelity, latency, and interoperability. The study concludes with strategic guidance for healthcare organizations and outlines future research topics, including automated twin generation and federated implementations. By aligning infrastructure with intelligence, the proposed model advances the promise of high-fidelity digital twins from concept to clinical reality.
Keywords: Digital twin, Healthcare, data mesh, master data management (MDM), real-time processing
AI-Driven Data Mesh with Generative AI for Enterprise Analytics (Published)
This article explores the transformative integration of generative AI capabilities with Data Mesh architecture to revolutionize enterprise analytics. Beginning with examining traditional data architectures’ limitations, the discussion highlights how centralized proceeds towards creating bottlenecks that impede innovation and time-to-insight. The Data Mesh paradigm is presented as a fundamental shift that decentralizes data ownership while maintaining federated governance. The integration of generative AI within this framework enables natural language interfaces, synthetic data generation, automated documentation, and intelligent insight creation. Implementation strategies using Databricks platform capabilities demonstrate how organizations can balance domain autonomy with enterprise interoperability. The architecture delivers enhanced analytics through AutoML-powered data quality with generative explanations and event-driven processing that enables real-time, predictive intelligence. Together, these capabilities create a self-improving ecosystem that democratizes data access while ensuring governance, ultimately enabling organizations to move beyond traditional reporting toward autonomous, data-driven operations with cross-domain collaboration.
Keywords: Real-time Analytics, data mesh, domain-driven architecture, federated governance, generative AI