Building and scaling a digital twin requires a three-step approach: creating a blueprint, building the initial digital twin, and then boosting its capabilities.

Create a blueprint

Aligning stakeholders on a clear vision of digital twins is a crucial first step. A blueprint should define the types of twins the organization will pursue, the order for building them to maximize value and reusability, the way their capabilities will evolve, and their ownership and governance structures. Without all this, we’ve seen companies build disparate single-use twins with limited engagement by the business and no way to attribute value from use cases back to the twin. The answers to eight key questions can help leaders create this blueprint.

Build the base digital twin

With the blueprint in place, the project team then builds the basic digital twin over three to six months. The build phase begins with assembling the core data product. To do so, data teams engineer structured and unstructured data to ensure their quality and usability. This in turn enables the development of visualizations and allows data science professionals to build out one or two initial use cases that generate additional data and insights—and create an early digital twin.

Organizations don’t need perfect data or a state-of-the art technology platform to get started. We’ve seen companies with varying levels of data and platform maturity successfully build digital twins. There are, however, several keys to building a successful digital twin.

Boost capabilities

Once the digital twin’s initial use cases are up and running, it’s time to expand its capabilities by adding more data layers and analytics to support new use cases. At this stage, companies often advance their twins from simply representing assets, people, or processes to providing simulations and prescriptions through the use of AI and advanced modeling techniques.

Digital twins have already become critical business tools for leading companies. However, the technology is accessible for any organization, no matter their level of digital sophistication. As a result, we expect digital twins will soon become key tools for optimizing processes and decision making in every industry. Executives are not only investing in digital twins today but also regarding the enterprise metaverse as a matter of “how soon” rather than “if.” In the near term, these efforts can provide tremendous value, enabling companies to curate data—just once—for hundreds of use cases that deliver deep insights on complex business issues and optimize outcomes in real time. In the longer term, these investments lay the groundwork for the enterprise metaverse that will transform how every organization in every industry operates and unlock the full promise of data and AI.