In previous articles, I discussed how Synthetic Data and Digital Twin are transforming how industries build, produce, and manufacture. AI can be trained with synthetic data tools from a small sample of real data. In contrast, you can use digital twins to generate hypothetical scenarios for evaluating performance, cost, and sustainability trade-offs. Both of these technologies are already making waves in the digital world. By 2024, it is expected that 60 percent of the data used to develop AI and ML models will be synthetic data rather than real-world data [1]. Furthermore, countries such as Singapore have begun to embrace digital twin technology, creating the world's first digital twin of the entire metropolis [2].

On a surface level, these two technologies may look poles apart. Although Synthetic Data and Digital Twins are indeed different, they are alike, as both use algorithms to simulate data for Artificial Intelligence and the communication models between AI and people. A digital twin can be created using the data from a physical entity to generate synthetic data. However, their use cases and market focus tend to vary. While Synthetic data tools focus on improving AI development workflows, Digital Twin applications aim to enhance large-scale product development, manufacturing, construction, and medicine. Since synthetic data and digital twins are closely integrated with a technical infrastructure that embeds AI, they have a lot of potential to work in tandem, improve workflows, and enrich synthetic and digital twin data.

Let’s read to know how a combination of synthetic data and digital twins can be beneficial across industries.

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1. Enhance Judgment - Synthetic components during the design phase can help designers explore various parameters of a digital twin and study the impact from different vantage points. For example, a synthetic modeling component can help designers study and build/improve calamity-resistant building structures. It may be helpful for designers or architects to visualize when planning to build structures in an urban landscape or improve infrastructure. Use cases may include creating virtual models of park running tracks, stadium queues, or even the optimum angle of sun rays on a solar panel.

2. Improve Healthcare Management - Digital twins in healthcare could overcome privacy issues by using synthetic data. Synthetic data is enabling the collection and analysis of healthcare datasets without privacy concerns. This results in the creation of digital twins that can develop healthcare models: the ones that can validate whether a model developed for a specific set of the population retains its accuracy when applied to other populations. Utilizing these technologies will ensure compliance with governing bodies such as HIPAA. In addition, it will enable healthcare and insurance providers to assimilate and analyze data for accurate research and development quickly.

3. Streamline Supply Chain Solutions - Synthetic data helps assess weaknesses in the supply chain and improve customer service through analysis of purchasing behavior. When multiple scenarios are run, it is possible to identify new products, risks, and remarkable partnership opportunities. Based on the methods, digital twins can test the viability of a product, assess risks, and use synthetic data based on real-world applications to create true-life replicas of products and identify gaps even before pushing the product to the supply chain.

4. Bolster Data Protection - When synthetic data and digital twins are combined to develop AI models that can identify anomalies in applications and data. Synthetic data and digital twins work together to allow AI engines to provide predictive analytics that helps customers gain insight into infrastructure reliability. In addition to assisting in planning or upgrading infrastructure needs, their combination ensures system uptime and identifies potential intrusions by reviewing several parameters and data.

5. Augment Consumer Experience - Digital twins allow us to understand better how users interact with enterprise software products, such as whether they use a particular feature, what notifications they choose to receive, and whether they collaborate with other users. Later, it is possible to aggregate, anonymize, and synthesize this usage data to drive automated tests and improve product roadmaps and customer satisfaction.

6. Fortify Privacy – A combination of digital twins and synthetic data can help companies train their AI models to forecast failures and take pre-emptive measures for their privacy. Renowned data management company Veritas is already preparing its AI models to detect anomalies in appliances and data.

Conclusion

Data relating to customers, various use cases, design options, equipment, and even incidents can be created as synthetic data sets to build a digital network for risk analysis, maximizing opportunities, identifying best choices, minimizing risks, and optimizing decision making. A combination of Digital Twin and Synthetic Data is particularly beneficial as it is cost-effective, improves complex virtual ecosystems, helps minimize errors, and makes more informed predictions by testing new scenarios and projects. Industrial enterprises already using synthetic data can train their workforce to ensure that the data can be fed to a digital twin to garner further insights and deliver better, safer outcomes.

References:
[1] 27 Synthetic Data Statistics: Benefits, Vendors, Market Size, https://bit.ly/3I86ANQ
[2] Virtual Singapore, https://bit.ly/3t4kQTA