Companies generate synthetic data as a fast, scalable way to test software and train ML models without compromising user privacy. But that’s not all…
Table of Contents
Why Generate Synthetic Data?
How to Generate Tabular Synthetic Data
Benefits of Using Synthetic Data
Generate Synthetic Data Better by Business Entity
Why Generate Synthetic Data?
In the fields of software development and data science, large and diverse datasets are necessary for testing applications under development and training Machine Learning (ML) models. However, the inherent difficulty of extracting data from multiple systems and then masking it to comply with privacy regulations (like CPRA, HIPAA, and GDPR), often prevent software engineers and data scientists from accessing the data they need, when they need it. Additionally, actual data may be scarce, unbalanced, or nonexistent (e.g., for negative testing).
To overcome these obstacles, enterprises are turning to synthetic data generation. Generating synthetic data involves creating a dataset that shares the statistical properties and structure of real-world data but doesn’t contain any sensitive or Personally Identifiable Information (PII). This synthetic data can be used alongside, or in place of, real datasets.
While generative AI synthetic data models, in general, and GPT, in particular, have been getting more and more attention, they’re used for much more than language-based applications. For example, American Express relies on synthetic financial data to improve fraud detection.
And in the coming years, the popularity of synthetic data will only grow. In fact, Gartner predicts that by 2030, synthetic data will overshadow real data in AI models.
How to Generate Tabular Synthetic Data
When it comes to generating tabular synthetic data, techniques range from the very sophisticated to the very simple (shown below in order of complexity):
1. Generative AI
Generative AI synthetic data techniques are the most powerful and are rising in popularity, due to their ability to handle large volumes and distributions of data. Generative AI uses ML models, including:
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Generative Pre-trained Transformer (GPT)
GPT is a language model trained on large and diverse amounts of tabular data. GPT-based synthetic data generation tools learn and replicate patterns from the training data. They’re used to augment tabular datasets and generate realistic tabular data for ML tasks.
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Generative Adversarial Networks (GANs)
GANs use a "generator", which creates a realistic synthetic dataset, and a “discriminator”, which tries to tell the real data apart from the fake data. In their attempts to fool the model, such networks produce highly realistic synthetic datasets.
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Variational Auto-Encoders (VAEs)
VAEs use an "encoder", that summarizes the characteristics and patterns of the real data, and a “decoder”, that tries to turn that summary back into realistic data. For example, VAEs generate fake rows of tabular data that closely resemble the real deal.
2. Rules engine
Rule-based techniques generate synthetic data using pre-defined business rules. Intelligence can be applied to the generated data by referencing the relationships between the data objects, to make sure the relational integrity of the generated data is maintained.
3. Entity cloning
Entity cloning collects the data for a selected business entity (e.g., specific customer or loan) from all source sources, and then masks it and clones it inflight. Unique identifiers are generated for each cloned entity, making it ideal for creating the huge amounts of data needed for performance and load testing.
4. Data masking
Standard or test data masking retains the statistical characteristics and properties of the actual data, while safeguarding sensitive or personal information. It replaces confidential information with altered values, or pseudonyms, to comply with privacy laws while preserving utility.
Benefits of Using Synthetic Data
One of the biggest advantages of using synthetic data is user privacy. Using real-world data for testing and training can potentially expose sensitive information, putting companies at risk of data breaches and non-compliance with data privacy regulations. In contrast, synthetic data can be safely shared and analyzed without exposing PII. Synthetic data is also used in situations where real data is scarce or restricted.
Synthetic data examples include:
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Medical research, where researchers can study diseases without compromising the privacy of real patients whose medical records the synthetic patient data is based on.
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Financial analysis, where brokers can predict stock market trends without revealing the identities of the individual traders on whom the synthetic financial data is based.
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Fraud detection, where enterprises create broad, balanced, and diverse synthetic datasets – including both trusted and fraudulent transactions – to better detect suspicious behavior in the real world.
Synthetic data companies are also concerned with:
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Augmenting data
When it comes to data augmentation, synthetic data can be used for ML model training and synthetic test data can be used for software testing. By generating plausible instances that mimic the distribution of real data, it aids in diversifying input variations, ultimately improving the model's ability to handle different scenarios.
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Rebalancing data
Companies that know how to create synthetic data can rebalance imbalanced datasets, where certain classes are underrepresented. By creating synthetic samples for minority classes, the data can be distributed more equitably, resulting in less bias and better performance for all classes.
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Down-sampling data
In the context of down-sampling, synthetic data offers an alternative to traditional random removal, enabling maintenance of valuable information while reducing dataset size. However, careful consideration is critical to ensure that the synthetic datasets align with the domain's underlying patterns, striking a balance between increased data volume and preserved quality for optimal outcomes.
Generate Synthetic Data Better
by Business Entity
Entity-based synthetic data generation tools create realistic but fake data, whose referential integrity is always enforced. When synthetic data is generated by business entity (customer, device, order, etc.), all the related data for each business entity is always generated, contextually accurate, and consistent across systems.
Synthetic data solutions based business entities capture and classify all the fields to be generated, across all systems, databases, and tables. They enforce referential integrity of the generated data by serving as a blueprint for the data generator – regardless of the synthetic data generation technique(s) employed:
- Generative AI: The business entity approach is used to train the model to generate only the most accurate, consistent, and valid data.
- Rules engine: The data generation rule relates to each field in the entity schema, and can be auto-generated based on the field classifications.
- Entity cloning: One instance of a business entity is extracted from the source systems, and then masked and cloned as required. A unique identifier is attached to each cloned entity.
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Data masking: Masking is performed as a unit, to ensure the referential integrity of the masked data.
When enterprises use K2view for synthetic data creation, they maximize flexibility because all 4 techniques are supported via business entities.
Learn more about K2view entity-based synthetic data generation tools.
