When Real Data Runs Dry: Synthetic Data for AI Models

Countless AI initiatives stall at the words: “We don’t have enough good data.” Data is the fuel that keeps the AI engine running, but the harsh reality is that real-world data often arrives too late, costs too much, or exposes too much risk. High-quality, representative, compliant, and labeled real-world data are often insufficient or inaccessible.

Enterprises face a wide range of data challenges. In healthcare and finance, strict regulations limit the use of real customer data. Even when organizations have data, sharing or leveraging it can risk legal trouble and damage customer trust. Time and cost are other hurdles. Data scientists spend up to 80% of project time wrangling and labeling data instead of building models.

Rare edge cases that lie outside the normal operating parameters of an AI model barely exist in historical data. Training an autonomous car to avoid crashes means you need crash data, but you can’t stage thousands of real accidents. And finally, real datasets often reflect historical biases or incomplete coverage, such as customer data that skews to one demographic or sensor data that is mostly recorded on sunny days. Models trained on such data inherit these blind spots.

In the absence of data, AI teams are increasingly turning to synthetic data, artificially generated information that mimics real-world data.

Understanding Synthetic Data

Synthetic data is created by algorithms, simulations or rules, and designed to reflect the patterns and statistical properties of real data so accurately that AI models can learn from it as if it were genuine. When done correctly, synthetic datasets can be as good as or even better than real data for training AI models, as enterprises can tailor them to include the specific scenarios and annotations they need.

Gartner predicts that by 2026, 75% of data used in AI projects will be synthetically generated, and by 2030, synthetic data will completely overshadow real data in AI model training. The firm’s analysts have suggested that “you won’t be able to build high-quality, high-value AI models without synthetic data.”

While synthetic data is a promising solution, a lack of awareness or understanding about how it works and where it’s best applied keeps many from considering it a solution to the problem of lacking real data.

There are several ways to generate synthetic data, each with its strengths.

Generative adversarial networks (GANs): In this scenario, two neural networks are pitted against each other to generate new data samples that mirror real data. One produces fake data, while the other attempts to identify it. This approach results in increasingly realistic outputs. While GANs dominated early approaches, diffusion models have increasingly taken center stage, demonstrating a superior ability to capture fine details; however, they’re also compute-intensive.

Simulations and synthetic worlds: Modeling the real world inside a computer using physics engines, 3D modeling software, or agent-based simulations. For example, creating a virtual city to produce synthetic driving data for self-driving cars. These simulations enable enterprises to simulate rare events that would otherwise be difficult to capture in real life.

Rule-based: Writing scripts using rules to generate synthetic data is suitable for use cases that don’t require elaborate AI. For example, synthetic sales transactions can be created by defining rules such as assigning one to five purchase records with dates spread over a year following specific distributions to each synthetic customer. By encoding domain knowledge and using randomization, enterprises can produce a dataset that appears realistic.

These techniques are often combined, such as starting with real data for authenticity, using a GAN to augment it and then simulating extra edge cases that the original data did not cover.

Why Consider Synthetic Data?

According to Forrester, the majority of global businesses are already working on initiatives involving synthetic data. Here’s why: 

Scalability without sticker shock: Unlike real data collection, which grows linearly with effort, synthetic data can scale exponentially. Manual labeling of a single image can cost ~$6, whereas a synthetic version might cost only $0.06. Scale that to millions of samples, and the costs become prohibitive.

Privacy and compliance: Synthetic datasets can be engineered to remove personally identifiable information while retaining behavioral or operational patterns, offering a compliance-friendly alternative to utilizing raw patient or customer data. Studies show that synthetic AI can retain up to 99% of the original dataset’s utility while maintaining compliance with the new General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Imagine training sophisticated healthcare AI on incredibly realistic patient journey data without using a single actual patient record.

Coverage of edge events: Enterprises can use synthetic data to simulate rare or hazardous events, such as fraudulent transactions, network outages, or industrial failures, allowing them to train the model to handle the edge cases that matter most. Autonomous vehicle companies, such as Waymo, utilize synthetic data to simulate accidents and unusual road conditions, training the model to recognize and handle the unexpected in the real world.

Time to value: Financial institutions report a 40 to 60 percent reduction in model development time by using synthetic data to overcome regulatory barriers. Reducing dependence on lengthy data acquisition and cumbersome compliance reviews accelerates the development of AI.

Fairness and bias: Synthetic data can correct historical underrepresentation, enabling enterprises to consciously design datasets that are more balanced, fair and representative, rather than trying to remove the bias from flawed data.

Getting Started

While synthetic data is a promising solution for a lack of real-world data, it comes with a few caveats that companies should consider:

• The reality gap: Models trained exclusively on synthetic data may struggle with real-world inputs. This challenge can be overcome with a hybrid approach that blends real-world and synthetic data for better results.
• Bias amplification: If the data used to train the synthetic data generator contains biases, the synthetic data can inherit and sometimes amplify those biases.
• Validation changes: How do you know synthetic data is any good? Validating synthetic data is complex, requiring rigorous testing, specific metrics and a clear understanding of what “good” means for a company’s use case.
• Resource requirements: Generating high-quality synthetic data requires advanced knowledge in machine learning, data modeling and computational resources.

These considerations aren’t deal breakers but are essential to address before getting started. From there, companies ready to dive into synthetic data should:

• Start with the problem. Clearly define the business challenge that needs to be solved. Leading with the “why” will illuminate the “how” and “if” of using synthetic data.
• Prioritize quality over quantity. Focus on generating synthetic data that accurately represents key statistical properties relevant to the problem and reflects the data characteristics the AI model needs to learn.
• Integrate with MLOps pipelines. To harness synthetic data at enterprise scale, integrate it into MLOps pipelines with automation, versioning and continuous monitoring. Doing so transforms synthetic data from a niche technique into a reliable, governed, and scalable enterprise capability.
• Know when to bring in a partner. Navigating the nuances of generating, validating and operationalizing synthetic data can be complex. Consider partnering with organizations that have deep experience in data strategy, AI and these emerging technologies.

Looking Ahead

Synthetic data is becoming fundamental to the modern AI toolkit. Companies across industries are proving the value of synthetic data. JPMorgan Chase uses generative models to produce synthetic examples of fraudulent transactions to balance skewed datasets and improve fraud detection. In healthcare, Elevance Health, formerly Anthem, partners with Google Cloud to generate petabytes of synthetic medical claims data for advanced AI model training without compromising patient privacy.

Whether supplementing real-world datasets, enabling companies to experiment with models or safeguarding sensitive information, synthetic data is an answer to traditional data source constraints. As AI becomes more ingrained in business, expect to see sophisticated synthetic data generation techniques, easier-to-use tools that democratize access and wider adoption across industries.