Synthetic Data Basics
Synthetic data is generated by advanced Generative AI models trained on real-world datasets. These models learn the patterns, correlations, and statistical properties of the source data, then produce new, artificial records that are statistically identical to the original but contain no personally identifiable information (PII).
This makes synthetic data ideal for analytics, testing, and AI training while enabling privacy-preserving data use and broad sharing without compliance risks.
This makes synthetic data ideal for analytics, testing, and AI training while enabling privacy-preserving data use and broad sharing without compliance risks.
The definition of synthetic data describes how synthetic data is generated. Synthetic data is generated by AI trained on real world data samples. The algorithm first learns the patterns, correlations and statistical properties of the sample data. Once trained, the generator can create statistically identical, synthetic data. The synthetic data looks, feels and means the same as the original sample the algorithm was trained on. The synthetic dataset is a perfect proxy for the original, since it contains the same insights and correlations.
Experiment with synthetic dataDifferent Types of Synthetic Data and How They Compare
AI‑Generated (Sample‑Based)
- Trained on real datasets
- Preserves statistical properties
- High utility, no PII
Mock Data (Rule‑Based)
- No real sample required
- Templates, rules & randomness
- Low realism
AI‑Generated Mock (LLM-Based)
- Prompt‑driven
- More realistic than rules
- Lacks statistical grounding
AI-generated synthetic data is sample-based. It requires a real dataset as input, which is used to train a generative AI model. The model learns the structure, patterns, and statistical properties of the original data, then generates entirely new data points that reflect those properties. The result is synthetic data that closely mirrors the original dataset in terms of utility and behavior, while containing no personal information.
Mock data, by contrast, is created without reference to real data. It relies on predefined rules, randomness, or templates. Because it lacks real-world context and complexity, mock data often fails to capture the statistical nuance needed for advanced use cases like model training or analytics.
A recent development is the use of AI-generated mock data, made possible through large language models (LLMs). This approach does not rely on a sample dataset but uses prompt-driven generation to produce data that follows structural or semantic patterns. While it can be more flexible and realistic than rule-based mock data, it still lacks the statistical grounding that sample-based synthetic data provides.
There’s also an important distinction between structured and unstructured synthetic data. Unstructured synthetic data includes images, audio, or video—common in areas like computer vision or speech recognition. Structured synthetic data, on the other hand, refers to tabular data with defined relationships between values, such as financial transactions, medical records, or behavioral time-series data. This type of data is widely used in enterprise systems and is particularly valuable for AI and analytics development.
Mock data, by contrast, is created without reference to real data. It relies on predefined rules, randomness, or templates. Because it lacks real-world context and complexity, mock data often fails to capture the statistical nuance needed for advanced use cases like model training or analytics.
A recent development is the use of AI-generated mock data, made possible through large language models (LLMs). This approach does not rely on a sample dataset but uses prompt-driven generation to produce data that follows structural or semantic patterns. While it can be more flexible and realistic than rule-based mock data, it still lacks the statistical grounding that sample-based synthetic data provides.
There’s also an important distinction between structured and unstructured synthetic data. Unstructured synthetic data includes images, audio, or video—common in areas like computer vision or speech recognition. Structured synthetic data, on the other hand, refers to tabular data with defined relationships between values, such as financial transactions, medical records, or behavioral time-series data. This type of data is widely used in enterprise systems and is particularly valuable for AI and analytics development.
How AI‑Powered Generation Works
Train
Deep generative models learn correlations, distributions, and structure from sample data.
Generate
Models create new records that are statistically and structurally faithful yet entirely artificial.
Protect
No one‑to‑one links to real subjects; re‑identification risks are eliminated by privacy protection features.
Validate
Automated QA checks utility and similarity; prevent overfitting and leakage.
Synthetic data generation is powered by deep generative algorithms. These algorithms use data samples as training data, in order to learn the correlations, statistical properties and data structures. Once trained, the algorithms can generate data, that is statistically and structurally identical to the original training data, however, all of the data points are synthetic.
Synthetic data subjects look real, but they are AI-generated and are completely artificial. When generating synthetic data, it's extremely important to prevent the algorithm from overfitting to the original data. Overfitting means the AI could potentially learn "too well", memorize original data and then accidentally leak original data points during the inference phase.
Not all synthetic data generators perform well and that's why you want to make sure to work with synthetic data generators of highest quality.
Synthetic data subjects look real, but they are AI-generated and are completely artificial. When generating synthetic data, it's extremely important to prevent the algorithm from overfitting to the original data. Overfitting means the AI could potentially learn "too well", memorize original data and then accidentally leak original data points during the inference phase.
Not all synthetic data generators perform well and that's why you want to make sure to work with synthetic data generators of highest quality.
What You Can Do With It
Drop‑in replacement for sensitive production data in non‑prod environments. Safely accelerate work across teams.
AI Training
Train and evaluate ML models with faithful, private data.
Train and evaluate ML models with faithful, private data.
Analytics
Enable exploration and BI without lengthy approvals.
Enable exploration and BI without lengthy approvals.
Testing & QA
Populate staging with realistic but safe datasets.
Populate staging with realistic but safe datasets.
Demos & Prototypes
Build and showcase products without leaking PII.
Build and showcase products without leaking PII.
Explainable AI
Stress‑test with outliers and diverse cohorts for governance.
Stress‑test with outliers and diverse cohorts for governance.
Synthetic Data is Safe
One of the key advantages of synthetic data generation is its ability to provide superior anonymization.
Unlike traditional methods such as masking, randomizing, or altering existing records, synthetic data is created entirely from scratch. It is generated by learning and reproducing the statistical patterns and correlations found in the sample data used for training.
Because there are no one-to-one relationships between synthetic records and real individuals, the risk of re-identification is effectively eliminated while preserving both privacy and data utility.
Learn more about how MOSTLY AI ensures privacy and securityUnlike traditional methods such as masking, randomizing, or altering existing records, synthetic data is created entirely from scratch. It is generated by learning and reproducing the statistical patterns and correlations found in the sample data used for training.
Because there are no one-to-one relationships between synthetic records and real individuals, the risk of re-identification is effectively eliminated while preserving both privacy and data utility.
How does Synthetic Data Compare to Other Data Anonymization Techniques?
Research has shown many times that legacy data anonymization techniques endanger privacy. They often also degrade the utility of the data.
Even worse - a lot of companies are equating pseudonymization with anonymization. But from a legal perspective, pseudonymized data is still personal data. And it needs to be treated and protected as just that.
Even worse - a lot of companies are equating pseudonymization with anonymization. But from a legal perspective, pseudonymized data is still personal data. And it needs to be treated and protected as just that.
Real life examples of synthetic data projects
AI-generated synthetic data is used by companies accross the world to accelerate their data innovation projects in a privacy-compliant and agile way.
Telefónica uses synthetic customer data for analytics
Erste Bank used synthetic test data to develop a successful mobile banking app
JPMorgan leverages a synthetic data sandbox to speed up data-intensive POCs with third party vendors
Anthem uses synthetic data to detect fraud and for delivering personalized service to members
How Good is the Quality of Synthetic Data?
A key question for any synthetic data generator is how accurate its output is. The data synthesis is therefore usually accompanied by an automated quality assurance (QA) process. The QA process checks if the synthetic data can be trusted to faithfully represent the original data. Each created Generator by MOSTLY AI comes with an automated Model Insight Report.
Learn more about how accurate the MOSTLY AI Synthetic data is and how it compares to other toolsSynthetic Data is More Flexible Than Real Data
“Synthetic data generation accelerates the analytics development cycle, lessens regulatory concerns and lowers the cost of data acquisition.” – Gartner
The Benefits of Synthetic Data
Data being the lifeblood of modern businesses poses enormous challenges to decision makers. Especially in regulated environments. On the one hand, data use is restricted by privacy, safety or other regulations. On the other hand, data access is critical in driving innovation. Synthetic data helps overcome this dilemma. The benefits of synthetic data are cost reduction, greater speed, agility, more intelligence and cutting-edge privacy. From transforming data access to AI governance, synthetic data generation can deliver high value use cases across organizations.
The potential of cross-industry collaboration
Synthetic data enables not only privacy-compliant data sharing within organizations. It enables a new level of cross-company and cross-industry collaboration - with huge economic benefits for everyone involved.
Synthetic data increases efficiency and profitability
The most important and immediately tangible benefit of synthetic data is that it helps speed up business processes and reduce bureaucracy. For data scientists, analysts and many more, it reduces the time to data massively and frees up their time to focus on value creation. It leads to leaner processes, higher employee loyalty and increased competitiveness.
Synthetic data is highly flexible
Felxibility is a synthetic data benefit agile teams can't work without. You can create and share synthetic data at will. It is as good as production data but much more flexible. You can modify the data, e.g. to correct for bias. You can downsize large datasets or create more data. The end goal is to increase data consumption across all of your teams in full compliance with the strictest data privacy regulations.
“By 2030, the majority of the data used for the development of AI and analytics projects will be synthetically generated”
- Gartner
- Gartner