What is Synthetic Data Creation?
Synthetic Data Creation Overview
Synthetic data creation, also referred to as synthetic data generation, is the process of generating artificial data or “fake data” that simulates real-world data in terms of characteristics and structure. Instead of gathering data from actual data records, synthetic data is created via the use of models and algorithms. The creation of synthetic data is critical for maintaining privacy protection, security, and data quality, particularly in the healthcare and finance industries.
FAQs about Synthetic Data Creation
How is Synthetic Data Created?
Creating synthetic data is a process that involves tools and algorithms that can differ based on the specific use case of the synthetic data. Three methods commonly used to create synthetic data include:
Generative models
Generative models are algorithms used in the replication of real-world data for creating synthetic data, while retaining the statistical properties or features. Generative models are able to generate new synthetic data, resembling the original data by learning the statistical patterns and relationships in the training data, then using this information to create the synthetic data. Generative Adversarial Networks (GANs) and variational autoencoders are examples of generative models.
Agent-based models
Agent-based modeling is a data simulation model where agents are created to communicate with each other. This technique is particularly useful for larger scale, more complex systems such as computer programs, applications, and mobile devices.
Distribution-based models
Another synthetic data creation technique is a distribution-based model. This approach is different from other synthetic data generation tools as it involves random number selection from an existing distribution set. The yield is similar as a new dataset is generated that is considerably comparable to real-world data.
What are Common Synthetic Data Creation Techniques?
Synthetic data creation is segmented into various techniques: Generative AI (including GANs), Data masking, Data augmentation, Rule-based engines and more. Here are some of the most common synthetic data creation techniques used today:
Generative AI
Using Generative Adversarial Networks (GANs), Generative Pre-trained Transformers (GPT), or Variational Auto-Encoders (VAEs) in modeling, generative AI detects the fundamental distribution of the real-world dataset and then generates synthetic data, maintaining the fundamental distribution of the original data.
Data masking
This technique replaces sensitive data, including PII, with fictional data that retains the overall structure of the original data. Data masking prevents sensitive data from being linked to any specific individuals, ensuring data privacy and protection.
Data augmentation
Data augmentation is used to artificially increase the diversity and size of a dataset by applying various transformations or modifications to the existing data. This technique is commonly used in machine learning (ML) and data science to address issues such as imbalanced datasets, insufficient data, or poor data quality.
Rule-based
Predefined rules are applied to replicate the structure and patterns of real data in this synthetic data creation technique. This method can be useful for simple scenarios where data relationships are well-defined.
Who Creates Synthetic Data, and Why?
Data professionals such as data scientists and machine learning (ML) engineers are common roles for synthetic data creation. Today’s industries that critically rely on the privacy and security of sensitive data include financial services and healthcare. Here, synthetic data creation techniques allow data professionals to generate synthetic data that preserves the characteristics of sensitive or regulated real-world data without compromising an individual's data privacy.
Synthetic Data for Financial Services
Fraud detection in the financial industry is a common use case for creating synthetic data. In many instances data scientists generate synthetic datasets to simulate credit card and debt payments in order to uncover fraudulent activity. These synthetic datasets can in turn be used for the testing of fraud detection methods and systems. Synthetic financial data allows institutions to meet the nuanced needs of modern banking and finance by providing private, safe data at scale. Learn more about synthetic data generation for financial services here.
Synthetic Data for Healthcare Services
Patient confidentiality is paramount in the healthcare industry. Healthcare data professionals use synthetic data to accelerate the development of clinical applications while safeguarding patient privacy. Synthetic data is generated to mimic real EHR without including any actual patient information. Synthetic healthcare data can also help in simulating patient data for clinical trials, facilitating more efficient initial testing phases and protocol development. Learn more about synthetic data generation for healthcare services here.
Synthetic Data for Web and Application Development
DevOps teams create synthetic data for software testing; and training, fine-tuning, and evaluating model performance. These data professionals create high-quality datasets such as text-to-code examples for popular programming languages like SQL and Python. With synthetic data, businesses can streamline AI development processes with high-quality, domain-specific synthetic data faster and more cost-effective.
Synthetic Data Creation Summary
Synthetic data is changing how industries, enterprises and data professionals are navigating regulations and compliance governance. Data professionals are increasingly turning to synthetic data creation to enhance data privacy, scalability, and cost-effectiveness. Synthetic data enables data teams to accelerate their AI and ML initiatives while adhering to regulatory requirements. Synthetic data creation not only provides data professionals an alternative to real-world data, it is a vital component of data privacy, security, testing, and more.
Does Gretel Provide Synthetic Data Creation Solutions?
AI developers trust Gretel’s synthetic data generation platform to create anonymized and safe synthetic data at scale. Gretel seamlessly integrates with leading technology companies and frameworks empowering data teams to innovate faster while preserving data privacy. Data teams can develop, test, and train AI models that learn the statistical properties of your data, then validate models and use cases with Gretel’s quality and privacy scores.
Learn more about safe synthetic data creation with Gretel here.
