Generative AI Synthetic Data Techniques: Leverage the Power

Learn why synthetic data based on generative AI is a safe efficient alternative to real-life data that's revolutionizing how we provide risk-free insights.

Table of Contents

Using Generative AI for Synthetic Data Generation 
How Does Generative AI Produce Synthetic Data? 
Why Does AI-generated Synthetic Data Matter?
Benefits of Using Generative AI for Synthetic Data 
Synthetic Data Generation Challenges
Generative AI by Business Entity

Using Generative AI for Synthetic Data Generation

Generative AI has been a hot topic since the release of ChatGPT, but it has already been having an outsized impact in the world of data and analytics, especially when it comes to its ability to generate fake data.

We need massive amounts of data to fuel AI advancement but acquiring and managing production data can be a daunting task, especially when dealing with sensitive information or limited resources.

This is where generative AI and synthetic data come in.

Synthetic data is artificially created data that mirrors the statistical properties, characteristics, and patterns of real data. Although the datasets are realistic, they don't contain any sensitive or Personally Identifiable Information (PII).

Synthetic data generation based on Artificial Intelligence (AI) refers to the creation of artificial data using advanced AI algorithms. These algorithms are trained to analyze existing datasets and generate new data points that possess the same statistical characteristics and patterns as the originals. This data can be used for testing applications under development, training Machine Learning (ML) models, validating systems at scale, and more.

Get the IDC Report on the Vital Role of Synthetic Data. 

How Does Generative AI Produce Synthetic Data? 

Synthetic data can be created by deep Machine Learning (ML) generative models, including Generative Pre-trained Transformer (GPT) methodology, Generative Adversarial Networks (GANs), and Variational Auto-Encoders (VAEs). These algorithms learn from existing data to create entirely new synthetic instances that resemble the original dataset.

1. GPT is a language model trained on extensive tabular data, capable of generating lifelike synthetic tabular data. GPT-based synthetic data generation tools rely on understanding and replicating patterns from the training data, useful for augmenting tabular datasets and generating realistic tabular data for ML tasks.

2. GANs are based on "generator" and "discriminator" neural networks. While the generator creates realistic synthetic data, the discriminator distinguishes real data from synthetic data. During training, the generator competes with the discriminator to produce data that attempts to fool the model, eventually resulting in a high-quality synthetic dataset that closely resembles real data.

3. VAEs are based on an "encoder" and a "decoder". The encoder summarizes the characteristics and patterns of real-world data. The decoder attempts to convert that summary into a lifelike synthetic dataset. VAEs generate fictitious rows of tabular data that follow exactly the same rules as their actual counterparts.

Why Does AI-generated Synthetic Data Matter? 

According to analyst Gartner, “The AI-augmented software testing market continues to rapidly evolve in terms of vendors and capabilities. To maximize the impact of these tools on testing efficacy, software engineering leaders must evaluate vendor offerings based on each tool’s AI capabilities in specific software testing areas.”

Generative AI has the potential to revolutionize the way we share and use data by closing the gap on data utility and privacy. Until now, organizations, government agencies, and researchers have relied on data anonymization techniques to maintain user privacy when using datasets for research, analytics, or testing.

However, the more anonymous data becomes, the more it's scrubbed of its utility. All data privacy strategies need to contend with this trade off. However, generative AI could change all of that: Because algorithms can be trained to produce a synthetic dataset that's statistically and structurally identical to the original dataset, the utility is maintained while individual privacy is protected.

Benefits of Using Generative AI for Synthetic Data

• Democratizing access to synthetic data
  Generative AI reduces the barrier to entry for smaller businesses and startups by making it much more scalable and accessible for anyone in the organization to create high-quality synthetic data, including synthetic test data combined with test data masking for testing environments.

• Protecting data privacy
  One of the key advantages of synthetic data based on generative AI is its ability to safeguard data privacy. Since synthetic data isn’t real, it doesn't fall under data protection regulations, such as those contained in GDPR, HIPAA, or CPRA. Now, medical researchers can work freely with synthetic patient data, while market analysts can base their insights on synthetic financial data.

• Producing quality data at scale
  Generative AI-based synthetic data ensures data quality by automatically filling in missing values and applying labels, resulting in more accurate predictions. It can also be easily scaled up to provide the massive datasets required for training Machine Learning (ML) models effectively. In research-driven fields, like medicine and climate science, access to vast and diverse datasets is crucial for advancing knowledge and innovation.

• Enhancing data diversity
  AI models often struggle with achieving diverse data representation and proper balance, leading to biased or incomplete results. Using generative AI to produce synthetic data helps address this issue by providing an abundance of diverse data samples, thus improving the performance and robustness of AI systems across different demographics and scenarios. Generative AI can also be used to create additional synthetic datasets for underrepresented classes, ensuring a balanced training dataset and preventing bias in AI models.

Synthetic Data Generation Challenges

Despite its numerous benefits, synthetic data generation also has its challenges. For example, ensuring accuracy without undermining the utility of the original data is crucial, and privacy concerns must be addressed, particularly when extreme values are involved. When companies synthesize data using AI, one risk is that the algorithm could become too realistic and accidentally generate data points that are the same as the original dataset.

Another major challenge for enterprises is how to create synthetic data using generative AI at scale while maintaining referential integrity of all data elements, which may reside in multiple data sources.

This is why many organizations are now turning to synthetic data creation based on business entities.

Generative AI by Business Entity

When a generative AI synthetic data solution is based on a business entity approach, a variety of different data generation techniques (used alone or together) are supported, including a rules engine, entity cloning, and data masking. Business entities (such as customers, devices, orders, etc.) are automatically modeled based on the metadata from the source systems.

The business entity model serves as the blueprint for generating the synthetic data, resulting in a privacy-safe and high-quality alternative to real datasets. And, entity-base synthetic data generation tools enforce referential integrity in the target systems.

By leveraging the power of generative AI algorithms, organizations can unlock valuable insights, build better models, and foster collaboration while maintaining data privacy. 

Learn more about K2view entity-based synthetic data generation tools.