New Delhi, Dec. 9 -- Artificial Intelligence (AI) has become integral to modern enterprises due to its ability to generate insights, automate decision-making, and drive operational efficiency. The success of AI projects hinges on access to vast, diverse, high-quality data that boosts models and innovation across industries. However, organisations regularly encounter challenges with real-world data, such as stringent privacy regulations, limited availability, fragmented datasets, and encoded biases.
This is where synthetic data comes in-offering a viable alternative that addresses these data constraints. In light of organisations adopting AI, synthetic data is emerging as a key enabler powering the next phase of innovation. It is artificially generated to replicate real-world information, allowing organisations to train models at scale without exposing sensitive details. Synthetic data offers a way to overcome these hurdles of privacy, incompleteness, or bias by providing large, diverse, and privacy-preserving datasets on demand.
The global synthetic data generation market, valued at USD 1.3 billion in 2024, is projected to reach USD 9.7 billion by 2030, growing at a CAGR of 37.4%. With AI already expanding into highly regulated sectors like finance and healthcare, synthetic data is rapidly becoming a vital component of responsible and scalable innovation. The growing embrace of synthetic data sets the stage for how organisations are driving innovation in the AI ecosystem.
Scalability and Rare Event Simulation
Modern AI projects are often stalled by insufficient data, especially for rare or sensitive scenarios. For instance, detecting atypical financial fraud or diagnosing uncommon medical conditions requires large pools of edge cases, where real data is hard to obtain. Recent research shows that 45% of businesses report unstructured, fragmented data as the most significant barrier to AI success. Synthetic data takes on this limitation by generating vast quantities of diverse and tailored data on demand, supporting the training of large models. This capability enables organisations to fill gaps in what is already available, simulate rare events, and generate diverse edge cases at scale. Organisations embracing this shift improve core model performance and responsible experimentation.
Fairness and Bias Mitigation
Building on the ability to scale and generate edge cases, synthetic data is also helping correct bias and support ethical AI. Embedded bias in human-generated datasets remains a major obstacle, skewing both the efficacy and ethics of AI solutions. Traditional datasets often underrepresent minority groups or uncommon scenarios, resulting in persistent unfairness.
By intentionally oversampling underrepresented cases and simulating scenarios missing from historical records, synthetic data empowers teams to rebalance distributions and inspect bias models. This proactive approach can increase model inclusivity, establishing a more even footing for all users. With these targeted strategies, organisations are proactively fostering fairness and accountability in next-generation AI systems.
Privacy and Regulatory Compliance
Just as synthetic data mitigates bias and representation risks, it also helps organisations overcome governance and privacy constraints that often slow AI adoption. Real-world data comes with regulations-the need to mask, subset, or anonymise live data delays development and adds risk. Synthetic data sidesteps these constraints, being free from sensitive personally identifiable information yet true to real patterns. This advantage makes it ideal for industries where privacy and regulatory compliance are paramount. For example, a leading US telecom giant faced challenges in its data modernisation journey due to strict data residency laws prohibiting production data from leaving the country.
With over 100,000 tables to migrate and onshore-only testing driving up costs, the team used synthetic data to generate production-like datasets without sensitive details. This solution enabled offshore testing at double the speed and ensured 100% regulatory compliance. This risk-free approach supports more ethical and responsible AI development, enabling robust validation and secure deployment.
Accelerating AI Innovation
With regulatory hurdles removed, synthetic data opens the door to broader innovation and experimentation across industries. It democratizes access to high-quality information, enabling startups and non-incumbents to compete with established firms that traditionally monopolised massive proprietary datasets. Synthetic data gives way to agile prototyping and robust benchmarking-allowing AI models to be tested and improved rapidly across multiple iterations. This accessibility lets organisations with limited, legacy, or siloed data modernise and deploy advanced models even in fragmented environments. Whether simulating sensor data for autonomous vehicles or augmenting clinical records for hospitals, synthetic data ensures that teams can experiment, validate, and scale solutions faster than ever.
Looking Ahead
Synthetic data is redefining the boundaries of what's possible in AI. This technology empowers organisations to break free from legacy constraints and truly innovate. Synthetic data is going to overshadow real data in both scale and impact, as it enables ethical experimentation, accelerates model development, and democratizes access to cutting-edge AI across sectors. By blending real and artificial datasets, enterprises are building AI systems that are not only more robust and fair but also more adaptable and secure. The future belongs to those who harness the full power of synthetic data, setting new benchmarks for AI strategies.
Published by HT Digital Content Services with permission from TechCircle.