The myriad uses of big data continue to unfold as new methods of generating, parsing and combining it evolve. Consider the data generated from Internet of Things (IoT) sensors, or produced through artificial intelligence (AI), or existing as historic data. Modern industry can now capture data from these and other sources to create a “digital twin” – a virtual model that is essentially the intelligent counterpart to an actual, physical object. By monitoring the status of an object or process and using multiple streams of data in real time to study its digital twin, engineers gain insight on how to improve product lifecycles, streamline maintenance and hone optimization.
Digital twin technology is playing a major role in manufacturing where installed physical machines are compared with the predictive behavior in digital twin pre-productive models to enhance performance. Further, it’s being used in healthcare where quality algorithms are being developed based on data from a number of criteria to help improve patient outcomes. In the aerospace field, machine learning pulls from existing data to create a prediction, so the more data a system processes, the more it will learn.
Creating a digital twin requires a solid data foundation to support it – one comprised of a data integration platform and management software capable of unifying multiple distinct data streams simultaneously that can be leveraged in a digital environment.
Conceiving a digital twin strategy
The idea of digital twin technology revolves around the utilization of data. For some enterprises, this may mean investing in the expansion of their data foundation to include things like IoT-based sensors, a revamped IT infrastructure, and digital solutions that allow better data management and storage. A data integration platform that engages an organization’s entire digital ecosystem, for example, can serve as a single critical environment that allows digital twin-centric data to reside, converge and be managed.
Some enterprises may already have IoT sensors in place and the data foundation and IT infrastructure to support their digital initiatives. But does this framework offer the scalability and adaptability to continuously update digital twin data and provide a real-time window into the physical object that facilitates prognosis and adjustment on the fly? Many companies are using application programming interface (API) technology and management tools to connect disparate data streams and gain the flexibility necessary to tap into the advantages of digital twins.
Of course, any digital twin strategy would be pointless if there were no practical business reason for pursuing it. Organizations that are truly capturing the value of digital twin technology have done the requisite enterprise data soul-searching to determine why it’s important in the first place. They’ve analyzed their data goals and determined the impact of digital twin technology on their operations and costs. Perhaps more importantly, they’ve recognized how this technology, and the data foundation that supports it, will open up opportunities to create new and better products and services that will satisfy an increasingly fluid, data-dependent marketplace.
Establishing digital twins to reflect the status of actual products and processes represents big data at its best. IoT and AI can generate multiple streams of data from a physical object that can be used to build a virtual counterpart capable of being molded, tested and compared against real-time and historical data to perfect an ideal. But bridging the gap between the real and the possible means having the proper data foundation and integration platform in place. Enterprises that have invested in digital twin technology not only have a way to cut costs and create new revenue, they have the power to reshape the world with just a tap.
Originally posted at insideBIGDATA