A digital twin typically refers to a non-physical representation of a physical thing used for predictive analytics. This is most commonly known from the manufacturing space, where critical components may report usage data to their digital twin, such that the twin can be used to predict potential failures.
For example, a jet engine may report certain details of its use: hours, environment, etc. back to the twin, enabling more accurate determination (or prediction) of maintenance needs. This more accurate determination can save significant costs on routine maintenance while reducing the risk of failure.
Traditionally, very little. However, there is now the idea of recognizing that the models typically captured within an enterprise architecture would enable the digital twinning of the enterprise itself. In essence, a digital twin of the organization.
Digital twins, when applied to the enterprise, can present another opportunity of demonstrated value for the enterprise architecture practice. Digital twins can help you develop new recommendations and roadmaps, and when applied to models, they can virtually determine how different scenarios will play out. With a digital twin, you can efficiently test products, strategies, roadmaps, and models before making any real-world changes – how incredibly powerful and what a great way to be more efficient, effective, and innovative.
According to research published in Top 10 Strategic Technology Trends for 2019: A Gartner Trend Insight Report, “75% of the end-user organizations Gartner surveyed that were implementing Internet of Things (IoT) projects said they had already implemented digital twins or would implement them soon. 87% of end-user organizations implementing digital twins said they update digital twin data models as their physical assets and equipment evolve.” Technology leaders must plan for the future – and that means planning for the use of digital twins. The IT landscape is only going to become increasingly complicated and diverse, leading to increasing challenges and risks. Enterprise architects can mitigate risk and navigate complex IT issues through a digital twin’s real-time view of a company’s processes, IT portfolio, and assets.
Using a digital twin to test scenarios is an excellent way to achieve operational excellence, as well as prioritize investments in line with expected and achieved growth. This would be achieved by using a digital twin to capture information about the business operations and present valuable analytics to the business for decision making. In a mature case, this may include leveraging the monitored data, and running simulations utilizing the monitored data as well as data representing the trends of the actual data to determine potential revenue opportunities, or points of failure.
For example, MEGA was approached by a company that was expecting significant growth in the next few years. They were concerned with their ability to sustain their service offerings while managing the growth. The vision we had was to utilize a digital twin of the enterprise, collecting operational data related to the company growth, including indicators that were related to the concerns expressed by the company. Using this data, and the trends of that data, the company could then assess the effects of the growth on the organization and make the appropriate pivots and investments needed to sustain their vision.
I expect to see an increase in attention put on digital twins of the enterprise in the next 5 years. There will be a maturing of understanding what data needs to be collected and tested in order to derive valuable analytics. Additionally, the ease at which this data can be collected and managed will continue to increase with the increased adoption of technological sensors. As this maturing of both understanding the potential, and the ease at which data can be collected (or performance measured), we expect to see increased opportunities to expand on the potential benefit of digital twins of the enterprise.