Definition: Synthetic Data
Synthetic data is artificially generated information that replicates the statistical patterns of real-world data without containing any actual records of real people, transactions, or events.
Core characteristics of synthetic data
Synthetic data is produced by algorithms rather than collected from real-world observations, so teams generate exactly the volume of examples a model needs. Because no real record maps to a synthetic one, the technique helps sidestep privacy constraints.
- Generated by statistical models, simulations, or generative AI
- Preserves distributions, correlations, and edge cases of the source data
- Contains no directly identifiable real-world records
- Produced on demand to fill gaps in rare or sensitive scenarios
Synthetic Data vs. Anonymized Data
Anonymization masks identifying fields in real records, but the individuals remain in the dataset and can sometimes be re-identified. Synthetic data instead generates entirely new records from a learned model of the original patterns, so no entry corresponds to a real person. The two approaches are often combined: anonymized data trains the generator that produces the synthetic dataset.
Importance of synthetic data in enterprise AI
Synthetic data lets enterprises train and test AI systems without exposing production customer or patient records, which matters for regulated Mittelstand sectors like healthcare and finance. Gartner projects that by 2030, 60% of AI training data will be synthetic. Higher data quality in the source dataset directly improves synthetic output fidelity.
Methods and procedures for synthetic data
Generating usable synthetic data requires choosing the right technique for the data type and risk tolerance.
Generative modeling
Generative adversarial networks, diffusion models, and large machine learning models learn a dataset’s structure and sample new records from it, producing the highest-fidelity output for images, tables, and text.
- Train the generator on a representative, cleaned source dataset
- Validate output distributions against the original data
- Retrain periodically as business data shifts
Rule-based and simulation-based generation
Rule-based generators build records from defined business logic, such as valid postal codes or order value ranges. Simulation environments go further, modeling a process like a production line to generate synthetic sensor data and rare edge cases.
Data augmentation and hybrid approaches
Many teams combine synthetic and real data, training a base model on real data and augmenting it with synthetic examples of rare failure modes or scenarios too sensitive to collect directly.
Important KPIs for synthetic data
Measuring synthetic data quality requires metrics that capture both statistical fidelity and downstream usefulness.
Fidelity and utility metrics
- Statistical similarity to source data: greater than 90% on distribution tests
- Downstream model accuracy delta vs. real data: within 2-5 percentage points
- Coverage of rare or edge-case scenarios: 3-10x increase over real data alone
- Data generation turnaround: days instead of months
Strategic business metrics
Programs are judged on how much they cut dependency on scarce real data. The global synthetic data generation market is projected at roughly USD 710 million in 2026, growing close to 39% annually.
Privacy and quality metrics
Privacy risk is measured through re-identification testing, including membership inference attacks that try to determine whether a real record influenced the dataset. Quality reviews should confirm the output does not amplify biases in the original data.
Risk factors and controls for synthetic data
Synthetic data reduces certain risks but introduces others that require deliberate controls.
Re-identification and privacy leakage
A poorly trained generator can memorize and reproduce fragments of real records, undermining the privacy benefit synthetic data is meant to provide.
- Differential privacy techniques during generator training
- Regular re-identification and membership inference testing
- Limits on how closely synthetic records may resemble a real one
Model bias amplification
If the source data reflects historical bias, a generative model can reproduce or exaggerate it, propagating that bias into every model trained on the output.
Regulatory uncertainty
Whether a synthetic dataset counts as anonymous or personal data under GDPR depends on the residual re-identification risk. A DPIA is the standard way to document that assessment before a pipeline reaches production, particularly for high-risk use cases under the EU AI Act.
Practical example
A 210-employee automotive parts supplier in Baden-Wurttemberg needed to train a computer vision model to detect surface defects on machined components. Real defect images were scarce, since defects occurred in under 1% of production, and photographing customer parts raised confidentiality concerns. The company built a synthetic data pipeline from CAD models of its own parts, rendering thousands of defect variations under different lighting and angles. Within ten weeks the model reached production-grade accuracy without touching customer data.
- Thousands of labeled defect images generated overnight from CAD files
- No customer part geometry exposed outside the plant
- Rare defect types represented at controlled, balanced frequency
- Retraining on new part families in days instead of months of photography
Current developments and effects
Synthetic data is moving from a niche privacy workaround to a mainstream part of the enterprise AI data pipeline.
Generative AI accelerates synthetic data creation
Large language and diffusion models have made it dramatically cheaper to generate realistic synthetic text, images, and tables on demand.
- Foundation models generate domain-specific synthetic data with minimal fine-tuning
- Generation is increasingly built directly into the data pipeline
- Vendors now offer synthetic data as a managed service with built-in privacy testing
Synthetic data for large language model training
As high-quality public text becomes scarce, leading AI labs increasingly use synthetic data from other models to supplement later training stages.
Regulatory scrutiny is catching up
German and EU authorities are beginning to publish guidance on when synthetic data qualifies as anonymous, shaping how enterprises document their data governance programs.
Conclusion
Synthetic data has moved from an academic curiosity to a practical tool for training and testing AI systems without exposing sensitive production data. For Mittelstand companies facing data scarcity and strict privacy rules, it keeps AI projects moving without months of real-world collection. It is not a privacy silver bullet: quality, bias, and re-identification risks still need active management. As generative AI makes synthetic data cheaper to produce, it will increasingly sit alongside real data as a standard input to enterprise AI pipelines.
Frequently Asked Questions
What is synthetic data used for?
Synthetic data trains and tests AI models when real data is scarce, sensitive, or expensive to collect. Common uses include computer vision training sets, software test environments, and fraud detection models needing realistic but non-personal examples.
Is synthetic data compliant with GDPR?
Properly generated synthetic data with no meaningful re-identification risk generally falls outside GDPR’s definition of personal data, but this must be demonstrated, not assumed. German authorities expect a documented risk assessment, often through a DPIA.
Does synthetic data work for a company with 50 to 250 employees?
Yes. Mittelstand companies with limited historical data often benefit most, since synthetic data fills gaps that would otherwise take years to accumulate. Cloud services have lowered the entry cost significantly in recent years.
What does a synthetic data project typically cost?
Costs depend on data type and volume. A focused pilot generating synthetic tabular or image data for one use case typically runs in the low five figures in euros; ongoing production pipelines cost more.
Do we need our own data science team to use synthetic data?
Not necessarily. Many companies start with an external partner or a managed platform, then bring generation in-house once the use case proves valuable. Internal teams still validate output and sign off on privacy testing.
How is synthetic data quality validated before use?
Validation compares statistical distributions between synthetic and real data, tests model accuracy against a real-data baseline, and runs re-identification checks. Most organizations require sign-off from a data science lead and a privacy reviewer.
