<p><b>Businesses are keen on digital twins. To build one, they need a high level of data maturity.</b></p>

<h4>In the news</h4> <p>A funny thing happened while everybody was poking fun at Mark Zuckerberg: <a href="https://www.cognizant.com/us/en/insights/perspectives/the-here-and-now-of-the-metaverse-wf1361750" target="_blank" rel="noopener noreferrer">The metaverse</a> became a useful business tool, thanks to digital twins.</p> <p>Specifically, as <a href="https://www.weforum.org/agenda/2023/01/metaverse-biggest-impact-industry-davos2023/" target="_blank" rel="noopener noreferrer">this piece</a> from the World Economic Forum points out, digital twins have been embraced by the industrial sector, including auto, shipping and mining businesses, among others.</p> <p>A digital twin is a dynamic virtual representation of a physical asset, process, system or environment that looks and behaves identically to its real-world counterpart. By ingesting data and replicating processes, a digital twin can predict performance outcomes and issues that the real-world environment might encounter.</p> <p>Using powerful digital twin simulations, for example, aerospace companies could understand exactly how a new plane will fly long before tooling begins. As the WEF article notes, “many new factories exist just as much in the digital world as they do in the physical.”</p> <p>The competitive advantage of using digital twins may include speed to market, improved product and service quality, and more accurate predictions about customer behavior.</p> <p>It’s important to note what a digital twin is <i>not</i>. It’s not merely a CAD drawing or a 3D model, a simulation model, a common data environment or telemetry/visualization. Rather, it encompasses multiple sources of data and models, and processes this data in real-time.</p> <p>A true digital twin also functions throughout the entire development lifecycle, from design to service. That’s why creating a digital twin requires an integrated approach across the entire value chain.</p> <h4>The Cognizant take</h4> <p>Digital maturity is key to succeeding with a digital twin implementation, according to Aakash Shirodkar, a Senior Director in Cognizant’s AI &amp; Analytics Practice. “You need a supporting data infrastructure,” he notes, “and access to high-quality data from both testing and the real data environment.”</p> <p>A suitable use case is also a must-have. “Generally, complex or dynamic environments that benefit from real-time optimizations are good candidates for digital twins,” Aakash says.</p> <p>In complex situations, businesses will need advanced simulation and analytics skills to speed computing power. “If you were trying to create a twin of a manufacturing facility,” Aakash notes,&nbsp;“it would require significantly more computing power than twinning a single asset within the facility.”</p> <p>A major obstacle to realizing the full value of digital twins is the significant upfront investment required, as well as limited access to high-quality data. Additionally, “Many organizations struggle to decide where to start or identify the most relevant use case to begin with,” Aakash says. Organizations should choose a use case that’s not overly complex, and create a minimum viable product that can be expanded gradually.</p> <p>“It’s important to understand your starting position,” Aakash says. Using a digital maturity assessment, businesses can evaluate their strengths and weaknesses to determine where they need to build out infrastructure and skillsets.</p>