What is synthetic data?

What factors are driving a demand for synthetic data across industries? And what are the risks and benefits of using synthetic data for decision making? In this article, we'll discuss synthetic data’s vital place in our data-hungry AI initiatives, how businesses can use synthetic data to unlock growth and the ethical challenges yet to be solved.

It’s hard to believe, but the rise of artificial intelligence has, in some ways, created data scarcity. Not a shortage, per se. We have an astonishing amount of data that’s growing exponentially (estimates show that 120 zettabytes were created in 2023). And that number could more than double by 2027!

No, our current data problem is suitability, not quantity. Synthetic data – a product of generative AI – may be the answer for that.

A look at four basic types of synthetic data and how they’re often used   

• Synthetic structured data represents individuals, products and other entities and their activities or attributes – including customers and their purchasing habits, or patients and their symptoms, medications and diagnoses.
• Synthetic images are crucial for training object detection, image classification and segmentation. These images are useful for early cancer detection, drug discovery and clinical trials, or teaching self-driving cars. Synthetic images can be used for rare edge cases where little data is available, like horizontal-oriented traffic signals.
• Synthetic text can be tailored to enable robust, versatile natural language processing (NLP) models for translation, sentiment analysis and text generation for applications such as fraud detection and stress testing.
• Synthetic time series data (including sensor data) can be used in radar systems, IoT sensor readings, and light detection and ranging. It can be valuable for predictive maintenance and autonomous vehicle systems, where more data can ensure safety and reliability.

Creating synthetic data: When to use SMOTE vs. GAN

Generating data with business rules and business logic is not a new concept. AI adds a layer of accuracy to data generation by introducing algorithms that can use existing data to automatically model appropriate values and relationships.

Two popular AI techniques for generating synthetic data are:

1. Synthetic minority oversampling technique (SMOTE).   
2. Generative adversarial network (GAN).

SMOTE is an intelligent interpolation technique. It works by using a sample of real data and generating data points between random points and their nearest neighbors. In this way, SMOTE allows you to focus on points of interest, such as underrepresented classes, and create similar points to balance the data set and improve overall accuracy in predictive models.

GAN, on the other hand, is a technique that generates data by training a sophisticated deep learning model to represent the original data. A GAN comprises two neural networks: a generator to create synthetic data and a discriminator that tries to detect it. This iterative adversarial relationship produces increasingly realistic synthetic data, as the discriminator ultimately cannot easily tell the difference between synthetic and real data. The training process can be time-consuming and often requires graphics processing units (GPUs), but it can capture highly nonlinear, complex relationships among variables and thus produce very accurate synthetic data. It can also generate data at or beyond the boundaries of the original data, potentially representing novel data that would otherwise be neglected.

A test: Synthetic data versus anonymized data

SAS and a partner tested synthetic data’s viability as an alternative to anonymized data using a real-world telecom customer’s churn data set (read the blog post, Using AI-generated synthetic data for easy and fast access to high-quality data). Van Unen explained that the team assessed the outcome on data quality, legal validity and usability.

What they learned:

• Synthetic data retained the original statistical properties and business logic, including “deep hidden statistical patterns.” Comparatively, anonymization destroyed underlying correlations.
• Synthetic data models predicted churn similarly to those trained on original data. Meanwhile, anonymized data models performed poorly.
• Synthetic data can be used to train models and understand key data characteristics, protecting privacy by reducing and preventing access to original data.
• Synthetic data generation processes are reproducible. Anonymization is variable, inconsistent and more manual.

“This case study reinforces the idea that AI-generated synthetic data provides fast, easy access to high-quality data for analytics and model development,” affirms van Unen. “Its privacy-by-design approach makes analysis, testing and development more agile."

Ethical considerations of synthetic data

As synthetic data use becomes more widespread, synthetic data vaults will also become more prevalent. These shared repositories will foster collaboration, data democratization and cross-pollination of ideas. But they could inadvertently underwrite bias, hide data privacy infractions and perpetuate unfair data practices.

Contrary to popular belief, Lucini argues, synthetic data is neither automatically private nor privacy-preserving. If not implemented with the right controls and testing, synthetic data generation can still lead to privacy leaks. 

"Generative models can be a ‘black box.’ To ensure responsible use, they require rigorous validation, which the industry has not yet fully developed. We have to approach synthetic data with great care to avoid unintended consequences," says Natalya Spicer, a Synthetic Data Product Manager at SAS.

The right to privacy is black and white – we can regulate it, put rules around it, and everyone can be bound by those rules. Fairness and bias are not as simple to regulate. If those subjective decisions are left to individuals, the consequences could have long-term consequences. So we need enterprise-level governance until there are more comprehensive government regulations.

“We built SAS^® Viya^® to serve as an enterprise platform for the compliant use of data and analytics, which is crucial with the acceleration of AI and synthetic data,” says Spicer. “SAS Viya has full traceability regarding how models are created, all the way back to raw data and the models used to analyze its accuracy.”

The future of synthetic data and AI

As artificial intelligence and data science advance, synthetic data will become increasingly important. The synergy between synthetic data and emerging techniques will enable the creation of even more sophisticated and realistic synthetic data sets, further pushing the boundaries of what is possible.

Governance will play an important role as the use of synthetic data evolves. Organizations must implement robust governance frameworks, data auditing practices, and clear communication around the limitations and appropriate use cases for synthetic data. Policies for labeling and identifying the use of synthetic data will also become crucial to avoid misuse and misunderstanding. By embracing the power of synthetic data, data scientists can unlock new frontiers of innovation, develop more robust and reliable AI models, and drive transformation that positively impacts our world.