Businesses are keen on digital twins. To build one, they need a high level of data maturity.

In the news

A funny thing happened while everybody was poking fun at Mark Zuckerberg: The metaverse became a useful business tool, thanks to digital twins.

Specifically, as this piece from the World Economic Forum points out, digital twins have been embraced by the industrial sector, including auto, shipping and mining businesses, among others.

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.

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.”

The competitive advantage of using digital twins may include speed to market, improved product and service quality, and more accurate predictions about customer behavior.

It’s important to note what a digital twin is not. 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.

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.

The Cognizant take

Digital maturity is key to succeeding with a digital twin implementation, according to Aakash Shirodkar, a Senior Director in Cognizant’s AI & Analytics Practice. “You need a supporting data infrastructure,” he notes, “and access to high-quality data from both testing and the real data environment.”

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.

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, “it would require significantly more computing power than twinning a single asset within the facility.”

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.

“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.