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What is Synthetic Data?
Synthetic data is artificially generated information that reproduces the patterns, structure, and statistical properties of real-world data — such as text, images, or numerical records — without containing actual personal, proprietary, or confidential details.
By using synthetic data, organizations can reduce privacy risks and compliance challenges while ensuring there is sufficient, diverse data to support systems that interpret language, process information, and handle tasks like enterprise search, product or document categorization, workflow automation, and data analytics.
It is an ideal solution for addressing gaps in real data, avoiding exposing sensitive information, and maintaining consistent data compliance and quality for systems that rely on understanding complex patterns and relationships.
There is greater flexibility associated with using synthetic data, as it is created from scratch, in comparison to anonymized data — data derived from existing sources but stripped of identifying features. Synthetic data enables better representation of rare scenarios or balanced data distributions without using original records, helping organizations manage data responsibly while supporting large-scale, data-driven operations.
Types of synthetic data
Synthetic data is artificial information created to reflect the patterns of real data, helping enterprises protect data privacy, test systems, and improve business workflows. Different types serve distinct purposes across industries and processes.
- Fully synthetic data: All records are entirely artificial and don’t copy any real-world entries. Enterprises often use this type to develop analytics or risk models without exposing sensitive information.
- Partially synthetic data: Only specific sensitive fields in real datasets are replaced with artificial values, while the rest remains unchanged.
- Simulated data: This data replicates how business systems or processes operate, allowing safe testing without real-world risks..
- Augmented data: Enterprises expand existing datasets by creating new synthetic variations, helping stress-test systems and improve insights.
Augmented data vs. anonymized data vs. synthetic data
All three approaches help enterprises manage data privacy and AI performance, but differ in how they transform, or create, data to balance utility, compliance, and risk.
| Augmented Data | Anonymized Data | Synthetic Data | |
| Definition | Original data enhanced with new, derived features or values to improve AI accuracy and insights. | Real data stripped of personal identifiers to protect privacy while retaining statistical patterns. | Entirely new data artificially generated to mimic real data’s statistical properties without exposing actual records. |
| Business Advantages | Boosts model performance and insights without needing more raw data; helps tailor AI to enterprise needs. | Supports compliance with privacy laws; allows safe data sharing and analysis. | Minimizes privacy risks; enables data availability where real data is limited or sensitive. |
| Enterprise Challenges | Risk of introducing biases; increased complexity in governance and traceability. | Possible re-identification risks; utility may drop if data is overly masked. | May lack subtle nuances of real data; requires validation to ensure reliability for enterprise decisions. |
Understanding these distinctions supports aligning data strategies with compliance, performance, and risk management goals.
Synthetic data use cases
Synthetic data helps enterprises develop, test, and deploy AI systems without compromising sensitive data or regulatory compliance, driving adaptability and lowering costs across business operations.
Speed visual QA in manufacturing
Manufacturing teams improve automated vision inspection systems by training them on synthetic images depicting rare defects not often captured during production. Synthetic data helps these systems recognize subtle flaws in parts or assemblies, reducing costly recalls and ensuring regulatory compliance. This allows manufacturers to maintain quality standards without waiting for defects to appear naturally.
Protect health data in clinical trials
Pharma and healthcare organizations use synthetic patient records in clinical trial management systems to simulate trial cohorts without exposing real patient identities. This enables teams to validate analytics pipelines and share datasets across departments while maintaining HIPAA compliance. As a result, trial designs move faster, and regulatory submissions are streamlined, all while preserving patient privacy.
Enhance contract triage workflows
Legal technology platforms generate synthetic contracts to train document management systems that classify and route agreements for legal review. Synthetic data reflects diverse clauses, structures, and risk terms found in real contracts, helping these tools identify uncommon or emerging legal language. Legal teams can therefore achieve faster contract processing and more accurate risk flagging, reducing manual review time and legal exposure.
Test risk models in finance
Financial institutions produce synthetic transaction data to test risk modeling tools under varying market conditions. These synthetic datasets let risk systems simulate rare events like market crashes or unusual customer behaviors without disclosing sensitive client data. This enables more rigorous stress testing, enhancing resilience and regulatory compliance for financial institutions.
FAQs
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By using statistical validation against real data — comparing distributions, correlations, temporal patterns, and outlier frequencies — without exposing raw sources. Implementing differential privacy checks (a mathematical technique ensuring statistical insights while protecting individual data points) and simulating real-world scenarios to ensure fidelity while maintaining data confidentiality.
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Excessive synthetic-only training can lead to model collapse — degrading diversity and accuracy. Hybrid datasets preserve rare patterns and reduce error propagation, maintaining model robustness and relevance.
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Opt for partially synthetic when authentic context matters. For instance, retaining transaction dates while masking sensitive fields helps preserve system workflows and ensures testing validity in finance or compliance scenarios.
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Conduct bias audits across demographic and business attributes, then rebalance generation processes. Combine domain expertise with fairness metrics (quantitative measures for checking whether models treat groups equitably) to adjust synthetic samples and maintain equitable model outcomes.