Unpacking How Enterprises Can Source and Utilize Synthetic Data for AI

As AI adoption continues to accelerate, business leaders are navigating the complexities of this powerful technology. At the core of AI is the data that feeds the machine – where it comes from, who owns it, and its reliability, all impact AI’s effectiveness. However, concerns around data accuracy, privacy, and bias challenge AI’s full potential. According to Unisys’ recent report, The AI Equation: 2024 AI Business Impact Research, executives remain optimistic about AI’s promise but are wary of its risks, with 64% concerned about bias and discrimination in AI systems.

High-quality and unbiased data is critical to mitigating these risks. But with AI models consuming human-generated data at an unprecedented rate, researchers predict we could exhaust real-world data sources as early as 2026. This is where synthetic data comes in. Synthetic data is artificially generated bits of information that mimic real-world datasets while preserving statistical integrity. Unlike anonymized data, synthetic data contains no personally identifiable information, reducing privacy risks. It is already showing promise in scalability, bias reduction, and security.

In many cases, real-world data is incomplete, sensitive, or too costly to obtain at scale. Industries dealing with strict regulations or proprietary information often struggle to access the data needed to train AI models effectively. Synthetic data circumvents these limitations by generating realistic, regulation-compliant datasets that can be tailored to specific use cases. This not only accelerates AI development but also guarantees models are trained on diverse, high-quality inputs, leading to more accurate and ethical outcomes.

How to Create Synthetic Data

You can’t create something out of nothing. Synthetic data is derived from real-world data and conditions to create a separate data entity, and it can be generated using various techniques, including:

Rule-Based Simulations: Data is created using predefined rules, formulas or logical conditions to replicate real-world scenarios.
Statistical Methods: Algorithms use distributions and correlations from real data to generate statistically similar but non-identical data points.
Machine Learning Models: Advanced models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) learn patterns from real data and generate new, realistic data samples.
Agent-Based Modeling: Simulations of interactions between entities (e.g., customers, products) produce synthetic datasets that reflect complex behaviors.

However, you still need the human-in-the-loop to reality-test the generated results. Only subject matter experts (SMEs) can verify the accuracy of the models and simulations. These individuals are essential to using synthetic data. Typically, data stewards within the business unit—not technology teams—are tasked with this role. They have deep knowledge of the specific domain and can assess whether the synthetic data “seems within spec” and if it accurately reflects real-world conditions. SMEs ensure the data truly represents what it should, bringing contextual relevance and practical insights to the synthetic datasets.

Accelerating AI Innovation Across Industries

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Across industries, synthetic data is unlocking new possibilities by overcoming the limitations of traditional data sources. Synthetic data provides a means to scale innovation without compromising an organization’s privacy, security, or regulatory compliance. This flexibility opens new doors for industries to address complex problems, enhance AI models, and improve decision-making.

In healthcare, synthetic data enables researchers to generate datasets that mirror real-world health trends, allowing for accurate AI modeling without compromising patient privacy or violating strict regulations like HIPAA. This is particularly valuable for studying rare diseases, training diagnostic models, and improving treatment recommendations. Similarly, in financial services, synthetic data allows organizations to train models that assist financial advisors in guiding clients toward better financial decisions, such as making strategic investments or managing accounts, all without relying on sensitive client data. In pharmaceutical research, synthetic data helps address challenges like limited patient populations and slow onboarding by simulating control groups, enabling researchers to test hypotheses and accelerate drug development without waiting for large-scale patient data.

Empowering the Future of AI with Synthetic Data

As enterprises scale their AI initiatives, access to high-quality data remains challenging. Synthetic data offers a powerful solution; however, its effectiveness depends on thoughtful implementation, rigorous validation, and human oversight to ensure accuracy and alignment with real-world conditions.

By strategically using synthetic data, businesses can unlock AI’s full potential and drive innovation and improve decision-making. Enterprises that integrate synthetic data into their strategies as AI evolves will gain a competitive edge and lead the way in shaping responsible, high-performance AI systems that inspire trust and compliance.

About the author: Brett Barton is Vice President and Global AI Practice Leader at Unisys. In this role, he drives identification and adoption of innovative technologies in AI, machine learning and deep learning, while leveraging these advanced technologies to drive innovation and business value for Unisys clients and their customers. Brett joined Unisys in April 2024 from Slalom, where he was a global technology executive working with the company’s major clients on solving business challenges by leveraging cutting-edge next generation technologies and harmonizing those advances with modified ways of working to deliver actual realized value to his clients.