Data-driven decision-making is increasingly important to businesses—and as enterprises lean more heavily on AI platforms to support this, there’s never been a greater need for ongoing access to large amounts of high-quality, diverse, and secure data.

Organizations across different industries rely heavily on data to train machine learning models, make strategic decisions, and personalize customer experiences. However, obtaining real-world data can be challenging due to privacy concerns, regulation, cost, and the sheer effort required to collect and clean large datasets. This is where synthetic data comes in: it’s an approach to data generation that can transform how organizations access and use information.

Synthetic data is more than just an artificial stand-in for real-world data. It offers unique advantages that can make it even more valuable than naturally-collected data—that is, data generated from regular business processes, like customer preferences or market analysis. By generating realistic data that mimics the properties of real datasets, synthetic data helps enable organizations to overcome data scarcity, ensure privacy compliance, and accelerate the training of AI models.

What is synthetic data?

When it comes to a synthetic data definition, we’re referring to artificially generated data that replicates the statistical properties and structure of real-world data without containing any actual real-world information. Unlike data that is collected from the likes of sensors, customer interactions, or business transactions, synthetic data is created using algorithms, mathematical models, or simulations.

Synthetic data can be created for various types of information, including numerical data, images, text, and even time-series data. Its primary purpose is to serve as a substitute for real-world data in cases where data is incomplete, expensive to collect, sensitive, or unavailable due to privacy or security concerns.

Different types of synthetic data

Synthetic datasets are versatile and rapidly evolving across various industries, from healthcare to finance to autonomous systems. Depending on the generation process and the degree of real-world data involved, the influence of synthetic data is usually classified into two main categories: partial synthetic data, and fully synthetic data. Each type has distinct use cases, advantages, and challenges.

Partial synthetic data

Partial synthetic data is a hybrid dataset where some parts of the data are real, while others are artificially generated. This approach is particularly useful for multilingual models in scenarios where sensitive or incomplete real-world data needs to be preserved or anonymized while maintaining the underlying statistical structure.

Partial synthetic data generation involves identifying sensitive or missing values in a real dataset and using statistical models or machine learning algorithms to generate synthetic replacements. The goal is to ensure that the synthetic data in generative AI retains the statistical relationships and patterns of the original dataset. Through the blending of real and synthetic data, organizations can preserve the key insights and predictive power of their data while protecting sensitive information and improving overall data completeness.

Offering the advantages of both real and synthetic data, this method preserves the statistical integrity of the original dataset while protecting sensitive information, making it a valuable solution when real-world data is incomplete or difficult to access. However, partial synthetic data can introduce biases if the synthetic replacements don’t accurately reflect the real data. Balancing data accuracy and privacy protection can also be complex, requiring careful tuning to avoid inconsistencies. Additionally, if the synthetic replacements are not sufficiently randomized, sensitive patterns from the original data could still be inferred, posing a potential privacy risk.

Examples of partial synthetic data in action include:

Healthcare industry: Medical data is highly sensitive and governed by strict privacy regulations like the US Health Insurance Portability and Accountability Act (HIPAA). Yet, researchers often need access to patient records to develop predictive models for disease diagnosis and treatment. In such cases, partial synthetic data can be created by replacing personally identifiable information (PII), such as patient names and social security numbers, with synthetic values while preserving critical medical indicators (e.g. blood pressure, glucose levels). This allows researchers to study patterns in patient health without compromising privacy.
Customer behavior analysis: A retail company might want to analyze customer purchase patterns without exposing individual customer identities. Partial synthetic data can be generated by keeping product purchase details intact while replacing customer names, addresses, and payment information with synthetic values. This protects customer privacy while preserving valuable insights into buying behavior.
Financial transactions: Banks and financial institutions need to monitor transaction data to identify fraud. However, due to privacy concerns, they can't always share real customer transactions with third-party fraud detection services. Partial synthetic data enables them to replace sensitive details (e.g. account numbers and personal details) with synthetic values while retaining the structure of transactional patterns, helping improve fraud detection models without violating privacy.

Fully synthetic data

Fully synthetic data refers to datasets that are entirely generated by algorithms or machine learning models without using any actual real-world data points. Partial synthetic data retains some connection to real-world information, but this is fully synthetic data meaning it is created from scratch based on statistical models and patterns learned from existing data or defined mathematical rules.

Fully synthetic data is often created using one of the following approaches:

Statistical modeling: A model is built based on the statistical properties of real-world data (e.g. mean, variance, correlation) and then used to generate synthetic data points.
Machine learning models: Techniques like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer models are used to generate realistic synthetic data that mimics the complexity of real-world data.
Simulation: Synthetic data can be created through simulations of real-world environments (e.g. traffic simulations, customer behavior modeling).

Fully synthetic data tools are designed to mirror the statistical properties and structural complexity of real data without including any actual real-world information. This makes evaluating synthetic data highly valuable for model training, testing, and research where privacy and data availability are major concerns.

It enables the creation of large, complex datasets at a low cost without the privacy concerns associated with real data. Since no actual data is involved, it reduces the risk of data leakage or regulatory violations. It's especially useful for training machine learning models on rare or unusual events, helping to improve model performance in edge cases and less common scenarios.

However, synthetic data may not fully capture real-world data's complexity and subtle patterns, which can limit its accuracy. The model used to generate synthetic data could introduce biases or errors, affecting the reliability of the output—if the model itself is biased, those biases can be replicated and amplified in the synthetic data. The quality of synthetic data also depends on the sophistication of the generation algorithm; less advanced models may struggle to produce accurate results.

But what is synthetic data in AI? Examples of fully synthetic data in action include the following:

Autonomous vehicles: Autonomous vehicle manufacturers often use fully synthetic data to simulate driving environments (e.g. adverse weather conditions, unexpected pedestrian behaviors) to train driving models. Gathering real-world data for every possible driving scenario would be almost impossible, so synthetic data allows these companies to scale training without relying on real-world test cases.
Medical research and drug discovery: In drug research, generating synthetic patient data can help model disease progression and treatment outcomes without the need for human trials. For example, a fully synthetic dataset simulating the spread of a disease and patient responses to different treatments can accelerate drug development.
Retail and marketing: E-commerce platforms often use fully synthetic customer behavior data to test new recommendation algorithms. Instead of waiting for real customer interactions, synthetic customer data (representing different customer profiles and preferences) allows businesses to refine their algorithms before deployment.
Cybersecurity: Cybersecurity firms create fully synthetic attack scenarios to train AI models for threat detection. Instead of exposing the model to real attack data, which could compromise security, synthetic attack data can be generated to test and strengthen the model’s resilience against various threats.

Evaluating synthetic data for your operations

Both partial and fully synthetic data have valuable roles in modern data science and machine learning. Partial synthetic data allows organizations to maintain statistical accuracy and protect privacy simultaneously, while fully synthetic data opens the door to large-scale training and simulation with minimized privacy concerns.

By understanding the strengths and things to consider of each type, organizations can develop more effective data strategies, accelerate AI development, and unlock new business insights.

What is a synthetic dataset used for?

Synthetic data is used across a wide range of industries and applications, including the following.

AI and machine learning model training

Synthetic data helps train machine learning models when real-world data is scarce, incomplete, or biased. It allows developers to create large, balanced datasets for training, simulate rare edge cases, and reduce data class imbalance.

For example, a facial recognition model can be trained on synthetic faces with different lighting, expressions, and angles to improve accuracy and performance across diverse scenarios. Synthetic data can also simulate production line scenarios, machine failures, and quality control variations in manufacturing to train predictive maintenance models and improve production efficiency in large scale transformer models.

Data privacy and security

Synthetic data enables organizations to protect sensitive information while preserving the statistical properties of real data. It allows companies to anonymize customer data, comply with regulations like GDPR, and share data internally or externally without exposing real information.

For example, healthcare organizations can generate synthetic patient records for research purposes without compromising patient privacy, while in the energy industry, synthetic data can be used to train models for energy load balancing, renewable energy forecasting, and grid optimization. It can also be used by power companies to generate synthetic weather and consumption data, which helps them to simulate grid performance under extreme conditions.

Product development and testing

Synthetic data allows companies to test and refine products before launching them in real-world environments. Developers can simulate user behavior, test system performance under heavy loads, and fine-tune features based on synthetic user data.

For example, a mobile app developer can use synthetic user interaction data to help identify and fix bugs before release, or government agencies could use synthetic data to train AI models for resource allocation, public service optimization, and predictive policy analysis.

Medical research

Synthetic data helps medical researchers work with realistic patient data while protecting patient privacy. It helps enable model training for disease diagnosis, drug development, and treatment predictions without using real patient information. For example, synthetic patient records can simulate disease progression and treatment outcomes, helping to accelerate drug discovery.

Fraud detection

Financial institutions use synthetic data to simulate fraudulent transactions and improve fraud detection models. Synthetic data allows algorithms to recognize complex fraud patterns, detect anomalies, and adapt to new types of fraud. For example, banks can generate synthetic transaction data to train models on different types of credit card fraud.

Customer behavior analysis

Businesses use synthetic data to better understand customer behavior, improve personalization, and enhance marketing strategies. Synthetic customer profiles and interaction data can help refine recommendation systems and target specific customer segments. For example, an e-commerce company can create synthetic customer data to test product recommendations and marketing campaigns.

How to generate synthetic data: different approaches

Generating synthetic data requires sophisticated techniques to ensure that the artificial data accurately reflects the structure and statistical properties of real-world data. The most common approaches include:

Statistical methods: This approach involves defining the statistical properties of a dataset such as mean, variance, and correlation, and generating synthetic data points that match these properties. Statistical methods are particularly effective for creating structured tabular data and time-series data.
Model-based approach: Model-based approaches use predefined mathematical or computational models to generate synthetic data. For example, a weather simulation model can generate synthetic temperature and humidity data based on known physical laws.
Machine learning (ML) approaches: Machine learning models have become increasingly popular for synthetic data generation through fine-tuning as they can capture complex patterns and relationships in data. Popular ML-based methods include:
- Generative Adversarial Networks (GANs), which use two neural networks—a generator and a discriminator—that compete against each other to create realistic data. The generator creates synthetic data, while the discriminator evaluates whether the data is real or synthetic. Over time, the generator becomes better at creating realistic data.
- Variational Autoencoders (VAEs), which generate synthetic data by encoding the data into a compressed latent space and then decoding it back into synthetic data. VAEs are particularly useful for generating structured data and images.
- Transformer-based models, which are deep learning models that excel at handling sequential data (like language or time-series data). Transformer-based models are used to generate synthetic text, speech, and even code.

Advantages of synthetic data

A synthetic data platform offers several important advantages for businesses and AI development. It allows companies to generate large, realistic datasets on demand, cutting down the costs and challenges of real-world data collection. This is particularly valuable in AI and machine learning, where diverse datasets are essential for training accurate models. Additionally, synthetic data enhances privacy and security since it doesn’t contain real-world information, which enables companies to share and process data while maintaining regulatory compliance.

Synthetic data can also accelerate model training by providing high-quality datasets quickly, helping businesses to refine their models faster and respond more effectively to changing conditions. Fraud detection models, for example, can be trained on synthetic financial transactions that mimic real-world patterns without needing large amounts of real data.

Synthetic data also helps reduce bias in machine learning models by generating more balanced datasets. It allows developers to create artificial examples that reflect different demographics and behaviors, improving model accuracy and fairness. A facial recognition model trained on synthetic data, for instance, can include a broader range of ethnicities and expressions, resulting in more reliable and unbiased performance.

Disadvantages of synthetic data

While synthetic data offers many advantages, it also comes with challenges. One key issue is around data quality and reliability, as synthetic data may not fully capture the complexity and subtle patterns of real-world data. This can reduce model accuracy and limit its ability to generalize across different scenarios. Ethical and legal concerns may also arise, particularly in sensitive fields like healthcare and finance. If the real-world data used to generate synthetic data contains biases, the synthetic version may reinforce or even amplify those biases, leading to unfair or discriminatory outcomes.

Technical complexity is another consideration, as generating high-quality synthetic data requires specialized expertise, significant computational resources, and, in some cases, the careful fine-tuning of advanced algorithms like GANs, large language models (LLMs), and VAEs. The process can be resource-intensive; if the underlying models are flawed or biased, the synthetic data they produce could amplify those issues, affecting the performance and reliability of the resulting AI models.

Synthetic data: computer-generated information but very real benefits

Synthetic data has become a powerful tool for businesses looking to overcome data challenges, protect user privacy, and accelerate AI development. The ability to generate realistic and diverse datasets on demand helps give companies a strategic edge, supporting them to overcome issues like privacy restrictions and incomplete data. This not only works to improve the efficiency of AI model training, but also creates more balanced and representative datasets, which can reduce bias and improve overall performance.

While there are challenges around data quality and ethical considerations, the benefits of synthetic data often outweigh the risks. Ensuring that synthetic data reflects real-world patterns without introducing bias requires ongoing refinement of data generation techniques.

Advances in generative models like GANs, VAEs, and transformers are making synthetic data more reliable and versatile. Some regulations, like GDPR, already recognize synthetic data as a privacy-preserving tool—but only if it is properly anonymized. Businesses in sensitive industries have to ensure compliance while managing the ethical implications of synthetic data use.

As AI and machine learning techniques advance, the applications of synthetic data will likely continue to grow. From training self-driving cars to improving fraud detection, synthetic data is already supporting innovation in sectors such as healthcare, retail, and cybersecurity. For companies looking to maintain their competitiveness, synthetic data offers a potent instrument for improving AI model performance and revealing fresh perspectives, especially in fields where sensitive or limited real-world data is available.

Synthetic data has emerged as a transformative force in the world of data science and artificial intelligence. It enables businesses and researchers to generate large-scale, realistic datasets without needing to consider the challenges of real-world data collection, such as privacy concerns or data scarcity. With the provision of a secure and flexible alternative to traditional data, synthetic data has opened new doors for innovation in fields such as healthcare, autonomous driving, fraud detection, and customer behavior analysis. Businesses seeking to stay competitive should consider harnessing the power of synthetic data to help innovation and growth.

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