Most people have heard the term “synthetic” in one context or another. It refers to an artificial product. Whether we’re talking about synthetic sweeteners or synthetic motor oil, it’s something not created by nature — it’s engineered.
Join us for a deep dive into synthetic data. We’ll explore:
- What is Synthetic Data?
- How is it made?
- What’s its role in AI development?
- Where are its limitations?
What Is Synthetic Data?
Just like synthetic sweeteners or synthetic motor oil, synthetic data is designed to mimic the real thing. There are two generally accepted types of synthetic data: AI-generated synthetic data and traditional mock data.
- AI-Generated Synthetic Data: ML models train on real-world datasets. After proper training, these models learn rules and relationships. Models can then generate new fake data based on these rules.
- Mock Data: For decades, mock data was the only available form of synthetic data. People create it based on what they expect from real-world data. Mock data often lacks the finer nuances of real-world and AI-generated data.
What Is AI-Powered Synthetic Data Generation and How Does It Work?
AI-Powered synthetic data generation utilizes finely trained AI models. After training on real-world data, these models create new data, reflecting the patterns of the original data — all without revealing the original real-world data.
AI-generated datasets look real. Their structure (or lack thereof) is indistinguishable from the real thing. In schools and training programs all over the world, learners of all kinds need data to work with. Whether you’re learning Excel basics, SQL or financial modeling, you need examples that resemble the real thing. Real-world data yields the best insights into real relationships, but it’s not always a feasible choice. You don’t want students learning from your medical records or finances.
- Training on Real Data: An AI model trains on real-world datasets. Depending on your domain, this could include structured data like medical records or customer transactions or unstructured data like sci-fi novels and images.
- Learning Relationships: During training, a model learns relationships in the data — it’s not supposed to memorize the actual data. Accidental memorization could result in leakage or reproduction of the original data which is exactly what synthetic data is designed to prevent.
- Generation and Inference: Using learned relationships, the model can generate new data that reflects the rules of the original data. This allows the model to create new information such as hyper-realistic datasets, or even beautiful artwork — all while respecting the privacy and intellectual property rights of the original data.
General Overview of Technology and Tools
Synthetic data generation isn’t a fixed, one-size-fits all process. Depending on the type of data, required level of realism and use case, synthetic data generation techniques usually fall into any combination of the three categories listed below.
AI-Generated Data
Data gets created by using a generative model such as a transformer-based LLM like ChatGPT, or by more specialized models tailor-made for producing synthetic data. These models might create customer profiles, spreadsheets, images or even fiction stories. No matter the case, the process is always the same — an AI model creates new data using the rules it learned from the training data.
Simulations and Transformations
Sometimes, data can be generated by running simulations. This is common in a variety of industries such as autonomous vehicles, drug discovery and specialized healthcare. With simpler requirements, data can often be warped or transformed in order to obscure the original data. In both cases, the original data gets passed through any number of simulations or transformations to create the new data.
Privacy Preserving Techniques
Privacy is perhaps the biggest selling point of synthetic data. Methods like data masking and statistical modeling are often used to prevent leakage of the original data. When bound by strict regulations like privacy laws, these methods allow your company to create data without violating international privacy standards like GDPR.
Use Cases of Synthetic Data for AI
Synthetic data offers a variety of use cases. It gets used not only to protect the original data, but create new data for machine learning, projections and more.
- Data Scarcity: In industries like healthcare, data can often be scarce — especially when dealing with uncommon medical conditions.
- Projections: Especially in the finance industry, decisions are often made using projected future data. When AI generates synthetic data, it can be used in tandem with current real-world data to make sound, insightful decisions.
- Synthetic Environments: Autonomous systems — like self driving cars and even automated vacuums — can train in simulated, synthetic environments in order to make better decisions in the real world — avoiding catastrophic failure and loss of life.
- Synthetic Traffic and Behavior Patterns: Retail and eCommerce giants often use traffic models to predict where customers will be and when they’ll be there. This allows them to arrange products and displays accordingly for maximum sales.
- Cybersecurity and Attack Vectors: Synthetic data gives ML models the tools to create synthetic environments. This allows both AI and human cybersecurity professionals to practice defensive strategies in a sandbox without risking damage to critical systems.
- Training and Education: Human students and AI models can learn through properly anonymized and well-distributed datasets. This gives them better tools to make proper analysis when the time comes.
Synthetic data isn’t a replacement for real data, it’s a complementary tool that allows both humans and machines to practice decision-making in a safe, sandboxed environment.
Synthetic Data for Training ML Models
When training an ML model, you need access to vast, high-quality datasets. Real-world data isn’t always available. Even when it is, it’s not always the right choice. This is where synthetic data comes in.
These synthetic datasets allow teams to handle all sorts of scenarios where size and quality of your real-world dataset might be suffering.
- Rare Data and Edge Case Training: AI-powered synthetic data gives teams the ability to prepare models for rare conditions or provide support in niche industries. Imagine training a model on rare medical conditions. Your datasets are extremely limited. With synthetic data, we can expand the pattern to create data points not present in the original dataset.
- Balance Skewed and Incomplete Data: Real-world data is never perfect. When data is skewed or incomplete, synthetic data can be used to fill the gaps and balance the results. Your model can then be trained on a more complete picture — reducing the risk of a biased or underperforming model.
- Avoid Privacy Concerns: Privacy concerns when training AI are some of the largest gray areas in global regulation today. With synthetic data, your model can train on new data that reflects the original patterns without revealing the original entries.
- Accelerate Development: In a time crunch, datasets can be reinterpreted and expanded using synthetic data. If you’re training a model to diagnose a rare, but often fatal medical condition, you can’t afford to wait for data.
AI-Powered synthetic data gives teams the power to handle scenarios that would normally bottleneck or end a project. It’s not a replacement for the real thing, but it provides a crucial workaround for niche projects.
How Tools In This Space Work
AI-powered synthetic data platforms might have different dashboards and internal processes, but all of them follow a similar workflow. The end goal is simple: Create data that reflects the original patterns without revealing the original records.
1. Data Generation
Everything begins with a real-world dataset. A generative model then trains on it, trying to learn the relationships and patterns behind the data. From there, the model creates new points in the data that reproduce the original patterns.
2. Validation
After creating the data, it needs to be validated. This step ensures its integrity. Whether it’s reviewed by people or another model, tools and companies ensure the data reflects the original structure without recreating the original dataset. Many tools use built-in safeguards to flag privacy risks or duplication of the source data.
3. Integration
Once it’s considered valid, the synthetic data can be used just like any other data. At this point, whether it’s a spreadsheet with millions of entries, or a vast set of images, the data is ready to train your model. Some teams will even test the data side-by-side with real-world data to see how the model performs with each dataset.
Highlighted Product Features
Synthetic data generators often differ in terms of features, but they are all designed to carry the same basic benefits — scalability, bias reduction and cost efficiency. In data-scarce environments, these provide development and analytics teams with valuable lifelines.
Scalability
Imagine you have a small dataset with maybe a thousand entries — and to collect more, you need to wait (and pay) for more data. AI tools give you the power to generate a much larger set — say 100,000 entries — based on your original data. This gives you the power to actually train models, or even analyze the data.
Bias Reduction
Bias can wreak havoc on analytics and ML training. Imagine you’re given a dataset of people with different job titles and incomes. However, your data is heavily skewed toward high-paying, white collar jobs — 80% white collar and 20% blue collar. Relationships in the white collar entries can skew the entire analysis. AI-powered synthetic data can find trends in both categories and balance it out 50/50.
Cost Efficiency
Collecting and cleaning real-world data can be very expensive. Between compliance, licensing and labeling, your project can take a huge budget hit. Licensing fees alone can sometimes be astronomical. With synthetic data, you’re not using anyone else’s proprietary content. All of the data’s fictional, but it reflects the real-world patterns your model needs to train effectively.
Limitations of Synthetic Data Generation
Synthetic data — even AI-powered synthetic data — comes with built-in limitations. This is a complementary or supplementary tool, not a drop-in replacement for the real thing. It’s dependent on high-quality original data and it often misses real-world nuances.
With no original source, synthetic data can’t even exist. When the original dataset is small or low-quality, it might hold relationships that are too subtle for even ML models to see and recreate.
If a model is trained on synthetic customer data, it might understand customers as objects in the database, but it might fail to see finer details like customer trends based on demographics, living area and other socio-economic factors.
Synthetic data will not replace your real-time or historical data sources. Imagine training a model on synthetic artwork. You ask it to create an image similar to the Mona Lisa — without ever having seen it.
Notable Tools For Synthetic Data Generation
Mostly AI
Mostly AI focuses mainly on compliance and ethical data standards. If you’re looking to train an ML model using fully compliant synthetic data, they give you the power to build high-quality training data while keeping up with compliance standards. Mostly AI offers tools for compliance, bias testing and quality assurance.
Gretel.ai
Gretel is built around protecting privacy and the original data. Gretel’s designed to work well across structured and unstructured data. Whether you need a fictional customer database or free text data, Gretel is built to meet those needs. They give you access to LLMs that can edit, augment and even fill gaps in existing data.
Synthea
Unlike our first two examples, Synthea is not a sleek, new synthetic data startup. It’s an open source tool used for generating synthetic health record datasets yourself. On their site, they also allow you to download some of their synthetic datasets here. Synthea is a great open source tool for AI training without compromising patient privacy.
Conclusion
Synthetic data, whether AI-powered or old fashioned mock data is a useful but niche tool. It’s not some magic magic replacement for all your data collection needs. It can provide useful support to areas of machine learning that require the strictest compliance and privacy standards. If you need to anonymize, augment or expand an existing dataset, these tools might be right for you. Synthetic data will not substitute your needs for real-world data with finely nuanced subtleties.