Artificial intelligence is set to transform organisations across all industries, but access to quality data represents one of the key barriers to success.

Data has been the main fuel for the digital era, but in today’s AI-powered world, it’s more like the engine – driving intelligence. The organisation that has the largest volume, best quality and most unique data is going to be able to create the more powerful and accurate AI applications.

However, real world data is increasingly difficult to obtain, manage, and utilise effectively while maintaining compliance with regulations. Enter synthetic data – a powerful solution that’s transforming how businesses develop and implement AI technologies. This artificially generated information is becoming the unsung hero of AI development, particularly for organisations with limited access to data or struggling with privacy, regulatory, or cost barriers.  

What is synthetic data? 

Synthetic data refers to data which has been created artificially. It is an approximation of real-world data, replicating its characteristics based on true attributes, but excludes anything that could distort results or be personally identifiable.  

It accurately reflects the characteristics of real-world data and comes in different formats, including structured (artificial database tables, client records), unstructured (text, images, videos) or even synthetic users.  

Today’s data obstacles  

For many organisations the pathway to utilising AI applications is littered with data related challenges: 

• Privacy and regulatory issues – GDPR and general sensitivity around data privacy makes it hard to get hold of, and use, many forms of data for AI model development. 
• Data scarcity and quality issues – AI applications need vast quantities of data and in specialised industries, or for rare events, there might not be available data. 
• Cost and feasibility barriers – Collecting, sorting and tagging real world data can be expensive and time consuming, which can delay AI projects. 
• Inherent biases – unintentional biases can often be found in real world data, which can have an impact on reputation, or other outcomes, if it manifests. 

How synthetic data helps 

 You might be wondering about the benefits of synthetic data. Here are just a few.

Overcoming privacy challenges 

Synthetic data can be generated based on preexisting real world data but without using any personal or private information. By maintaining any statistical or other common attributes it can act the same as real world data but overcomes restrictive legal hurdles and ethical dilemmas. This is especially useful in regulated industries where data protection requirements are high. As synthetic data is essentially anonymous, it isn’t subject to any ethical and confidentiality constraints.   

In healthcare, patient data is heavily regulated under laws like HIPAA and GDPR, making it challenging to use real world datasets for research, AI model development, or clinical decision support. Hospitals and research institutions are turning to synthetic data as a solution – creating statistically accurate, yet entirely artificial patient records that mirror real world clinical scenarios without exposing any personal information. For example, organisations like MDClone work with health systems to generate synthetic datasets that preserve the patterns and relationships found in original patient data while fully eliminating the risk of re-identification. 

This approach allows healthcare teams to accelerate AI model development, test clinical workflows, and collaborate with external partners without facing the legal and ethical hurdles of sharing sensitive data. Researchers can explore complex questions – such as predicting disease progression or optimising treatment plans – using synthetic datasets that behave like real patient populations. As a result, these organisations can innovate faster while maintaining strict compliance with data privacy regulations. 

Addressing data imbalances 

In specialised industries or for rare events, there may simply not be enough real data available so synthetic data can supplement these gaps. This could cover scenarios such as under-representation from a particular group, mimicking an unusual event or creating test scenarios that would be unlikely to happen frequently enough to have good data for. Real world data can often have inherent attributes which lead to unfair or inaccurate outcomes, potentially causing financial harm and reputational damage. Synthetic data can be created to balance out shortfalls, giving a more representative dataset. 

Take, for example, the task of ensuring an autonomous car responds appropriately in untoward driving situations. There may be a lack of real-world data on hand to inform an AI model fully on all weather conditions. If, say, hailstorms happen infrequently in some locations, it might be difficult to capture enough live events for model training or find relevant historical data. In this scenario, synthetic data in the form of simulated images of falling hailstones could be used to mimic a host of situations that might arise infrequently but could lead to life-threatening consequences. 

Similarly, images of people or objects suddenly appearing in the path of the car could be computer-generated and tested from all angles, different sides, even from above and below a car, to ensure all eventualities are covered. Without this level of training, the model may not recognise a potentially hazardous situation, then accidents will happen, and lives will be in danger.  

Cost-effective 

For many organisations acquiring real world data can be prohibitively expensive. The process of collecting, sorting, and tagging data for AI training is often time-consuming, complex, and resource intensive. In contrast, synthetic data offers a cost-effective, predictable alternative. For businesses with limited budgets, it removes the upfront need for large-scale data collection and preparation, significantly reducing costs. The result being a more streamlined path to testing and deploying AI solutions. 

For example, J.P. Morgan has explored the use of synthetic data to improve fraud detection model development without relying on sensitive customer transaction records. Accessing and using real financial data typically requires costly anonymisation, compliance checks, and legal reviews – slowing projects down and driving up costs. By generating synthetic datasets that replicate real transaction patterns, J.P. Morgan reduced the need for expensive data preparation and minimise regulatory hurdles, making their AI projects faster, safer, and more cost-effective. 

Synthetically powered AI is here to stay 

For organisations striving to harness AI’s potential, synthetic data represents a pivotal solution to overcoming many of the barriers that slow development down. It addresses privacy and compliance challenges, fills critical data gaps, reduces costs, and helps eliminate biases – all while accelerating model training and validation. 

The market momentum is clear. Gartner predicted that by 2024, 60 per cent of the data used for AI development will be synthetic and have suggested that synthetic data will likely overtake real data by 2030 as the dominant resource for AI model training.  

For many organisations, this shift presents a major opportunity. Those who embrace synthetic data early will be better positioned to develop robust AI capabilities, deliver faster innovation, and remain compliant in an increasingly regulated environment. Synthetic data won’t replace real data entirely, but it will become an essential tool – enabling businesses to unlock AI’s potential at speed, scale, and lower cost. 

Geoff Barlow is product and strategy director at Node4. 

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