Robust machine learning models rely on high-quality, high-dimensional, and high-fidelity data. However, data is a scarce resource, and obtaining it is often a challenge. Sensitive customer and private data are heavily regulated under data privacy laws, while public data is almost always biased, imbalanced, and incomplete.
This puts enterprises in a challenging position. Either risk sensitive data breaches potentially leading to hefty fines and legal action, or invest significant resources into cleaning, organizing, and enriching public data.
The smart ones, however, take the third and right route, i.e., Differentially Private Programmatic Synthetic Data.
What is Synthetic Data?
At Betterdata, our differentially private synthetic data mimics real data's statistical properties, correlations, and nuances—without containing any personally identifiable information (PII).
No PIIs = No privacy risks = Unlimited, secure data usage and sharing.
Synthetic data is created through advanced machine learning models such as GANs, LLMs, VAEs, or DGMs. These models are trained on real data first and then generate synthetic data that looks, feels, and works exactly like real data.
This allows anyone generating synthetic data to,
- Augment synthetic data to improve domain coverage and model generalization.
- Enhance synthetic data to cover edge cases, remove biases, and balance datasets.
- Scale synthetic data to meet training data requirements for large-scale AI and ML Models.
Data Augmentation with Synthetic Data for AI and ML:
Solve Data Scarcity:
- Synthetic data can be augmented to simulate a wide range of scenarios, including rare or extreme events, improving domain coverage and helping models generalize better.
- Synthetic data can be augmented to increase the size of the training dataset, making it easy to scale artificial intelligence and machine learning models without running massive data collection campaigns.
Improve Data Quality:
- Synthetic data augmentation can be used to remove bias in a training dataset, addressing class imbalance, underrepresentation, overfitting, etc. Ensuring fair model performance across all domains, particularly in use cases like fraud detection, medical research, or hiring, where data is limited.
- Synthetic data can be customized to meet the specific requirements for different use cases and domains, which is especially helpful in fine-tuning AI/ML models.
Increase Efficiency:
- Differentially private synthetic data randomizes real data features while completely anonymizing PIIs, making it impossible for anyone to identify real user profiles. Therefore, it can be shared quickly with both internal and external stakeholders for collaboration, review, analysis, and feedback, accelerating model development.
- Synthetic data can be generated on demand by deploying synthetic data generation models to your data pipeline. This eliminates the need to run massive data collection campaigns regularly for training AI and ML models which require both time and money.
Improving Data Utility with Synthetic Datasets:
Traditional anonymization techniques work by masking, encrypting, or suppressing information. These methods essentially make it impossible for anyone to see or read sensitive customer data. This does not work for data scientists, AI, and machine learning engineers who either need to spend countless hours rebuilding the dataset or work off on a quarter of the information collected. This affects data utility, which then impacts artificial intelligence and machine learning model training.
Synthetic data is the complete opposite of whatever I wrote in the above paragraph. Synthetic data preserves data utility by not destroying data but rather generating structurally and statistically similar alternative data. Allowing data scientists, AI, and machine learning engineers to see all, read all, and share all while training high performing artificial intelligence and machine learning models.
Data augmentation with synthetic data is transforming the way enterprises approach machine learning. By generating diverse, representative, and privacy-preserving synthetic datasets, enterprises can avoid models trained on flawed public datasets or highly anonymized sensitive customer data, often ending up racist, sexist, or just plain wrong, leading to headlines we’d all rather avoid and enabling models to perform better in real-world scenarios.
