Establishing a Digital Twin Program and understanding the landscape (part 3)

Understanding Macro Challenges and Key Drivers

As with many digital transformation investments, a major consideration is data privacy and security. Digital twins generate and integrate vast amounts of data, including sensitive and confidential information about a business's operations, customers, or products. This information must be protected from unauthorised access, theft, or misuse. This may involve encryption, access controls, regular security audits, and compliance with relevant data privacy laws and regulations.

Another critical challenge is the cost of implementation and operation as both can be significant investments for an organisation. Businesses need to purchase hardware and software, hire skilled personnel, and allocate time and resources for development and testing.

The costs can be substantial depending on the complexity of the digital twin and the industry it is used in. That’s why it’s important to map out investments and run them by relevant stakeholders.

Another major challenge is integration with existing systems. Businesses must ensure the digital twin system is compatible with their existing infrastructure, data and workforce and can exchange data seamlessly. This may require additional development work, such as building APIs or modifying existing software and hardware systems and focusing closely on the transition of data governance models and workforce adoption.

Testing Awareness and Acceptance of Current Data Capability

A key priority is to define common data standards and connect previously disparate systems, so that a single, continuous, ‘live’ picture of your asset’s use and performance can be generated through the digital twin. Achieving this takes a combination of existing domain knowledge, digital skills, and some new IT investment as effective integration of all key elements is the central challenge.

Many service providers tout how their systems and apps provide interoperability, but it’s critical that your data is structured, accessible, and readily available to be transferred and read. Early incorporation allows for better data collection, more accurate modelling, and immediate feedback during the implementation and development phases.

At this stage, it’s useful to consolidate your data sources, apps, systems, and platforms to visually design the real-time flow of data, add in analytics, and trigger recommendations or workflows. Once you have the data stream set up, you can move on to creating the visual aspect of the twin that will provide situational awareness to your users – helping them understand what happened, why it happened, key trends, and recommended actions.

Conclusion

As you deploy your digital twin capability into the real world, you are bound to learn things you never expected. It might be more challenging to get access to the data. You might uncover that your operational problems are more significant than you thought. You could also learn that this twin could be scaled out to solve this same issue across multiple departments or sites in your organisation for even greater ROI.