The demand for high-quality, diverse, and secure datasets for machine learning (ML) has increased so quickly that traditional sources of data are having trouble keeping pace. With privacy regulations getting stricter, real-world data expensive to source, and companies needing to train more and more complicated models, the industry is shifting to synthetic data—artificially generated data that looks, acts, and behaves like the real thing.

In this blog post we are going to discover what synthetic data is and why it is important, the benefits and challenges, how it can be used, and why it has been dubbed the “new fuel” for machine learning in 2025.

What is Synthetic Data?

Essentially, synthetic data is data that has been synthesized rather than measured from a real world event and/or people. The goal instead is to model the distribution, structure, and relationships of a real data set while avoiding the actual risk to the privacy and/or any sensitive information related to people.

For example:

• A bank can create a synthetic version of a bunch of financial transactions to train a model for detecting fraud without risking exposure of its customer’s real financial accounts.
• A hospital can generate a bunch of synthetic patient records needed to do AI diagnostics and avoid dealing with legal and ethical issues over HIPAA or GDPR.
• A retail brand can create synthetic customer profiles to use with a recommendation engine and before actually implementing it to millions of people.

Synthetic Data can be generated in different ways:

• Rule-based methods: Developers define data’s parameters and distributions, and subsequently generate synthetic data based on these characteristics.
• Simulation models: Data is generated from modeling environments where data is generated through simulation (e.g. self-driving cars interacting with traffic in a virtual world).
• AI-generated methods (e.g. GANs, VAEs, LLMs): The latest types of generative artificial intelligence methods generate data that is often indistinguishable from data that is sourced from the real world.

Why Synthetic Data is Booming in 2025

There are multiple forces that have converged to make synthetic data more relevant than ever: 

1. Privacy and Regulations – Data privacy regulations like GDPR (Europe), CCPA (California), and India’s DPDP Act have made it more difficult to collect real data as they create complexities if businesses mishandle personal data. Businesses can suffer massive fines from regulatory agencies if they mishandle personal data, synthetic data avoids that risk because it does not necessarily relate back to real people.
2. Data Hungry AI Models – AI models, especially large models like LLMs (large language models) or computer vision models, need massive volumes of data. Collecting, labeling, and cleaning data at scale takes a long time and costs lots of money. Synthetic data, on the other hand, can be massively scaled almost endlessly in an infinite cycle to continuously create “fuel” for training.
3. Cost – The actual cost of acquiring real-world datasets can run into millions of dollars. For example, autonomous vehicles need enough data of real-world driving experience to capture rare things like accidents or extreme weather conditions. A synthetic environment can create thousands of scenarios in hours and for a mere fraction of the cost.
4. Bias and Fairness – Real world data is often biased and skewed towards a certain gender, race, or demographic. Synthetic data can be purposely balanced like a “test” group of sorts, and this can help reduce discrimination in the AI system.
5. Safe Testing and Simulation – Some scenarios (e.g., – cybersecurity breaches, medical mistakes, car accidents) are dangerous or unethical to create in reality. Synthetic data enables a safe environment to experiment in as there are no real-world consequences.

Key Applications of Synthetic Data in Machine Learning

Synthetic data is not a science experiment and is already radically changing fields. Let’s look at some impactful uses of synthetic data: 

1. Healthcare: By law, hospitals and researchers cannot always share applicable real patient records. Synthetic data sets enable researchers to develop artificial intelligence models for disease predictions, drug discovery, and personalized medicine without violating privacy policies. For example, artificial intelligence models trained on synthetic chest X-rays, have shown to be equally clinically accurate as those trained on real chest X-rays. Therefore, despite being based on synthetic data, they are still valuable for early diagnosis.
2. Autonomous Vehicles: Companies need billions of miles of driving data for self-driving cars so they can prepare for a myriad of unpredictable situations. Companies such as Tesla, Waymo, and NVIDIA will build synthetic traffic scenarios from a range of things from sudden pedestrian crossings to extreme weather as it relates to the simulated driving environment.
3. Finance and Fraud Detection: Banks and fintech companies use synthetic data to generate millions of financial transactions to train their fraud-detection algorithms. Since real-life fraud cases are few and far between, synthetic data helps simulate “attack patterns” without the risk of exposing customers’ accounts.
4. Cybersecurity: Businesses generate synthetic attack data – for example, phishing attempts, malware patterns, denial-of-service attacks – to train intrusion detection systems. The goal is to prepare the AI models for attacks before they occur in the real world.
5. Retail and Marketing: E-commerce sites generate synthetic customer behavior data to test their recommendation engines. Synthetic clickstreams, browsing and purchasing patterns support a fast-tracked optimization of the algorithms without needing access to actual customer logs.
6. Manufacturing and IoT: Manufacturers generate synthetic machine sensor data to pattern equipment failure. Synthetic IoT data can synthesize years of operational trends in a matter of hours, enabling predictive maintenance models to be built in less time.

Benefits of Synthetic Data

Synthetic data is touted to be the “new fuel” for ML in 2025 due to the following reasons:

1. Privacy-Safe: There is no identity link to any real people involved.
2. Scalable: Within seconds, generate millions of samples.
3. Cost-Effective: Minimize own costs in collecting and labeling.
4. Bias-Rigging: Intentionally bias the datasets.
5. Free & Clear: Publish datasets without the bannermen of legal risk.
6. Flexible Testing: Train models on rare, risky, or more exotic situations. 

Challenges of Synthetic Data

With its potential, there are some drawbacks to synthetic data: 

• Quality Control → Synthetic data must be realistic enough to capture just enough of the complexity of the real-world. If poorly generated synthetic data causes ML models to be trained off of misrepresented data, the models can become mislead from the start. 
• Subtle Representation → Synthetic data could miss the subtleties or more intricate sensations. Lack of subtleties can lead to weak generalizing ability. 
• Ethical Dilemmas → If the original “seed data” has bias baked in, any synthetic derivatives will also have that same bias baked in proportionally.
• Trust & Acceptance → Even in 2025, there are organizations that still do not feel comfortable to be utilizing these datasets because they fear synthetic data won’t be nearly as accurate as developing an acceptable, reproducible test with real-world data.