Digital twins are redefining how enterprises connect data and operations. Understanding their value and how Fabric enables them, is key to making them a practical advantage.
Understanding Digital Twins
What if enterprises could test decisions in a virtual environment before executing them in reality? That is the core promise of digital twins, not just observing operations but simulating them with accuracy and context.
Digital twins create data driven and real time representations of entities, whether physical assets like machinery, logical entities like customers, or dynamic processes like manufacturing and logistics. They act as a bridge between the digital and physical worlds and provide organizations with visibility, context, and control.
Why They Matter
Digital twins allow organizations to:
• Anticipate failures before they happen and reduce downtime and disruptions
• Improve quality and optimize maintenance schedules
• Continuously monitor and fine tune operations to drive efficiency and lower costs
• Enhance decision making with faster and data backed actions
These capabilities explain why digital twins are becoming a strategic differentiator across industries such as manufacturing, logistics, healthcare, energy, and smart cities.
Microsoft’s Approach with Digital Twin Builder
Building and managing digital twins at scale has historically been difficult. Fragmented data, inconsistent quality, and governance challenges slowed adoption.
Microsoft Digital Twin Builder, now in preview, addresses these issues by embedding twin capabilities directly into Fabric. It provides a low code and no code environment where decision makers can model assets and processes in a semantic model that represents their physical environment.
Within this environment, data from sources such as Fabric OneLake, IoT feeds, and historical records can be unified. Semantic relationships connect assets, processes, and sites in a coherent structure. Once modeled, the data can be explored with built in visualization and query tools, and large datasets such as time series or maintenance logs can be analyzed even when they span long periods of time.
This makes digital twins available not only to data scientists but also to operational teams who need real time insights for decisions on the ground.
Governance and Security
Digital twins are only as reliable as the data they are built on. By embedding them within Fabric, organizations inherit enterprise grade data management. Lineage, permissions, and compliance controls are applied consistently across the semantic model. This ensures that insights are not only fast but also auditable and secure.
From Data Models to Real Time Intelligence
What makes Fabric powerful is that digital twins are not isolated silos. Once mapped, they become part of the Fabric lakehouse architecture and are accessible across the enterprise like any other trusted data asset.
Through SQL endpoints, digital twin data can be queried as if it were a traditional database. This makes it familiar to analysts and easy to integrate into existing workflows. When connected to Power BI, twin models can be visualized in real time dashboards that bring operational intelligence into daily decision making. Real Time Dashboards extend this further by allowing organizations to monitor live activity, trigger alerts, and act at scale.
This combination of historical and real time data transforms digital twins from conceptual models into operational tools. Fabric allows organizations to use the same OneLake foundation, SQL queries, and Power BI dashboards they already rely on, while extending them into predictive and forward looking scenarios.
Looking Ahead
Business intelligence focuses on past and present states. Digital twins simulate the consequences of change. They move analytics from retrospective reporting to predictive and prescriptive intelligence, guiding organizations toward optimal actions.
As IoT, AI, and real time analytics converge, digital twins will evolve into a core element of enterprise design. Microsoft Fabric stands out by offering a unified environment where predictive and simulation based capabilities are not separated but embedded directly into everyday decision making.
