It’s a universal truth that the more accurate your data management, the more accurate your data-driven insights. This maxim is at its most apparent when those insights are powering frontline operations, whether in logistics, energy, or large-scale manufacturing.
Digital twins have emerged as one of the most powerful ways to turn this operational data into intelligence. Essentially, they act as sophisticated virtual replicas of physical systems like factory lines, shipping fleets or depot networks. When fed with high-quality information, these models can predict maintenance needs, optimise production processes, accelerate sustainability goals, reduce costs, and improve output quality. And adoption is rising fast, with at least 70% of industrial companies expected to have at least one digital twin in operation by 2026.
But building a digital twin doesn’t guarantee impact. Without the right trusted unified data at their core, digital twins risk becoming noise rather than intelligent decision-making engines. In most cases, the challenge isn’t a lack of data from sensor readings and system logs but an inability to parse it. For example, raw sensor signals like pressure, vibration, and temperature may signal what is happening, but not why, or what action should follow. A digital twin needs deeper, trusted context if it’s to become a true decision-making engine.
Digital twins that work
This is where master data and metadata play a critical role. The former captures the core business entities and processes, including assets, equipment hierarchies, parts, and suppliers, while metadata provides a broader view of how systems, applications, and data sources are connected. Together, they supply the meaning and relationships that elevate digital twins from monitoring tools to intelligent, AI-powered models.
When organisations rely on digital twins built on shallow or inconsistent data, they expose themselves to missed faults, misplaced parts, uninformed teams, and costly downtime. In environments involving heavy machinery or remote operations, a digital twin without trusted data isn’t just inefficient; it can be dangerous.
Operational clarity, over chaos
Real value comes from bringing together the information that typically sits scattered across enterprise resource planning (ERP) platforms, computerised maintenance management systems (CMMS), and supervisory control and data acquisition (SCADA) systems. Breaking down these silos creates a single, consistent view of assets and their performance.
Connecting real-time sensor signals with the business data that gives them relevance is crucial. Throughout this process, strict data cleansing, validation, and governance ensure that AI and machine-learning models operate on reliable, secure information.
The benefits of this approach are already being realised. One global energy operator managing widely distributed renewable and gas assets across multiple sites discovered that inconsistent data made it almost impossible to gain a single view of maintenance schedules, delays, or supply-chain responsiveness.
By establishing a unified, well-governed data foundation, they turned their digital twins into a daily operational advantage, getting real-time visibility into their asset performance across the world. They cut maintenance costs and spare parts inventory by 20%, boosted on-time delivery by 30%, reduced unplanned downtime by 8%, and improved their supply chain responsiveness by 15%.
For organisations managing large numbers of physical assets, digital twins can unlock a whole world of efficiency, resilience and intelligence. But any digital twin project must start with reliable and unified asset data. Without this fully connected and context-rich foundation, value will be hard to find amidst the noise. But with it, digital twins become powerful engines for operational improvement, smarter, safer business decisions and differentiation.
Greg Hanson is the group vice president and head of EMEA North at Informatica
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