Synthetic Data: Generate what reality cannot provide
Synthetic data creates new datasets when real-world data is scarce, sensitive, imbalanced, expensive to collect or difficult to label.
LEAF designs generation and validation pipelines around the task the data has to support. We evaluate utility, fidelity, coverage and disclosure risk before synthetic data enters training, testing or simulation workflows.
Why Synthetic Data
When reality does not provide enough data.
Many AI and analytics projects are constrained by the dataset before they are constrained by the model. Important classes may be rare. Sensitive records may be difficult to share. New systems may not yet have generated enough history. Edge cases may be expensive or unsafe to reproduce.
Synthetic data creates controlled additional observations around those constraints. It can augment real datasets, support testing and simulation, or make early development possible before enough production data exists.
It is not automatically accurate, representative or private. Its value depends on how it is generated, what it is meant to support and how rigorously it is validated.
Data Modalities
Different data needs require different generators.
The generation strategy depends on the structure of the original data and on how the synthetic dataset will be used.
Tabular data
Structured business, operational and transactional datasets where distributions, correlations, constraints and rare categories need to remain useful for downstream analysis.
Time-series & sensor data
Temporal signals, telemetry and industrial observations where trends, seasonality, transitions and abnormal conditions matter.
Sensor data at the edge: AI on the EdgeText & document data
Controlled textual examples for evaluation, classification, retrieval and language workflows when real material is limited or cannot be freely reused.
Image & vision data
Generated or simulated visual samples for training and testing when collecting or annotating enough real images is difficult.
Modalities and generation methods are selected per project. Not every technique is appropriate for every dataset.
The Method
Generate. Measure. Challenge. Integrate.
01/ Define
Start from the task
Define what the synthetic dataset has to support: training, testing, simulation, rare-event coverage, data sharing or early prototyping. The target task determines what “good” means.
02/ Generate
Build the synthetic population
Select and configure the generation approach around the data modality, constraints and available real samples. Generation can augment a real dataset or create a controlled standalone dataset.
03/ Validate
Measure more than realism
Compare synthetic and real data at statistical and task level. Evaluate distributional fidelity, downstream utility, coverage, bias and disclosure risk.
04/ Integrate
Use it where it earns its place
Introduce the synthetic dataset into the target workflow only after evaluation. Track the effect on model, test or simulation performance and revisit the generator when the underlying data changes.
Validation
Synthetic does not mean useful by default.
A dataset can look realistic and still be useless for the task. It can preserve statistical patterns and still expose too much about the source data. Validation therefore needs several lenses.
Fidelity
Do distributions, relationships, temporal patterns and constraints behave like the source data where they need to?
Utility
Does the synthetic dataset actually support the downstream task: training, testing, simulation or analytics?
Coverage
Does generation improve representation of rare, difficult or strategically important cases without distorting the rest of the dataset?
Disclosure risk
How much information about the source data can potentially be inferred from the generated samples? Privacy risk must be evaluated rather than assumed.
Bias
Which biases in the original dataset are reproduced, amplified or reduced by generation?
Robustness
Does performance remain stable across different seeds, samples, scenarios and evaluation splits?
Where It Helps
More control over the data bottleneck.
Data scarcity
Extend development when there are not yet enough observations to support the target task.
Rare events
Increase controlled exposure to infrequent but important classes, faults, anomalies or operating conditions.
Privacy-constrained development
Reduce dependence on direct access to sensitive source records while still evaluating disclosure risk explicitly.
Private infrastructure: Confidential AITesting & QA
Create repeatable datasets for software, data pipelines and AI systems, including conditions that are difficult to reproduce on demand.
Simulation & digital environments
Generate controlled scenarios for systems that need to be exercised before enough real-world history exists.
Dataset augmentation
Complement real datasets with additional examples where measured utility improves the downstream model or analysis.
The Principle
Synthetic data works best when reality stays in the loop.
Synthetic data is rarely valuable because it replaces every real observation. Its value is usually in extending what reality can provide: balancing rare classes, creating scenarios that are difficult to collect, reducing dependence on sensitive records and making testing more controllable.
Real data remains essential for grounding and validation. The generator, the synthetic dataset and the downstream model all need to be challenged against the real system they represent.
Limits
What synthetic data does not guarantee.
The objective is not to create data that merely looks convincing. It is to create data whose usefulness and risks can be measured.
- Synthetic data is not automatically private.
- Realistic-looking data is not automatically statistically faithful.
- Higher similarity to real data is not always better if it increases disclosure risk.
- Synthetic data can reproduce or amplify bias from the source dataset.
- A generator validated for one task should not automatically be trusted for another.
- Synthetic data does not eliminate the need for real-world validation.
- Regulatory or compliance suitability depends on the context, data and deployment and must be assessed separately.
Need data you cannot collect directly?
Tell us what is missing, what the dataset needs to support and which constraints make real data difficult to obtain. We will assess whether synthetic data is the right tool and how it should be validated.