industryA strong data foundation for your digital twin is non-negotiable
Share this article Copy Link Share on X Share on Linkedin Share on Facebook A digital twin is only as good as its data, argues Informatica’s Greg Hanson. (Image: Gorodenkoff / Shutterstock) 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 mon