Data management professionals face growing challenges in handling the vast amounts of live data generated by live systems, such as transportation networks, power grids, and security infrastructures. IoT devices have proliferated widely and produce real-time data on an unprecedented scale, placing significant demands on traditional data processing methods. Legacy approaches – often reliant on batch processing and siloed databases – struggle to keep up, resulting in an inability to saudi arabia rcs data respond in the moment to dynamic issues. New technologies, including digital twins and generative AI, promise to address these challenges. Used in combination, they can deliver real-time analytics and breakthrough decision-making capabilities to assist in the management of today’s complex, live systems.
Digital Twins Offer Powerful Analytics and Prediction Capabilities
Digital twins are emerging as a powerful tool for managing the complexities of live data. A digital twin is a software-based, virtual representation of a component in a physical system. While originally created for product design and evaluation, it also can be used to track live behaviors and continuously monitor for emerging issues. A complex system, such as an airport or smart city, might have millions of digital twins representing many different types of components.