There’s a lot to learn from real-world synthetic data examples in highly regulated industries like financial services, healthcare, retail, and e-commerce.
Table of Contents
Why is Synthetic Data Important?
Types of Synthetic Data
Real-World Synthetic Tabular Data Examples
Synthetic Data Examples Based on Business Entities
Why is Synthetic Data Important?
Synthetic data is artificially generated data that is designed to mimic real-world data. Because it doesn’t contain any sensitive or Personally Identifiable Information (PII), it’s a valuable substitute for real data with several advantages, including privacy preservation and data augmentation.
For software development and data science, synthetic data has emerged as a game-changing solution to testing software under development and training ML models, where scale matters.
Types of Synthetic Data
Before we delve into the world of synthetic data generation, let's look at a few examples of what format synthetic data can take.
The simplest form of synthetic data is random data generated with a specified distribution. For example, you can generate random numbers following a normal distribution, uniform distribution, or any other desired pattern.
There are 3 basic types of synthetic data:
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Synthetic tabular data
Synthetic tabular data refers to artificially generated values structured in rows and columns, representative of the real data found in databases or spreadsheets. Synthetic tabular data is great at mimicking various types of sensitive data – like synthetic patient data, derived from actual healthcare databases, or synthetic financial data, based on real banking or trading records.
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Synthetic text
Synthetic text refers to artificially generated text created through Machine Learning (ML) models. While generating realistic text has historically been challenging due to language complexities, recent advancements in large language models, such as GPT (Generative Pre-Trained Transformer), are revolutionizing text generation. These models can produce human-like text, making synthetic text invaluable for many different applications.
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Synthetic media (image/video/sound)
Synthetic media encompasses artificially rendered image, video, or sound files that closely resemble their real-world counterparts. This similarity allows synthetic media to serve as a drop-in replacement for production data in various scenarios. For example, synthetic video data can be used to train ML algorithms when real video data is unavailable due to privacy concerns or data scarcity.
Theoretically, synthetic data solutions vary based on the use case. For example, in the business world, synthetic social media data can model connections between users, posts, and comments, for moderation purposes. Or in eCommerce, synthetic data can be used to simulate product catalogs, user interactions, and purchase histories for predictive analysis.
In fields like robotics or IoT, synthetic sensor data can be generated to simulate sensor readings. This can include data from temperature sensors, cameras, accelerometers, and more.
Synthetic time series data can be used to mimic real-world temporal patterns, which can be useful for testing predictive models or forecasting algorithms. Or for geospatial data, it can simulate locations, routes, and features like maps, roads, and landmarks.
Real-World Synthetic Tabular Data Examples
Here are some examples of how enterprises are using synthetic tabular data across various industries:
Synthetic Data in Financial Services
For the financial sector, synthetic data is used for risk management, credit risk assessments, and compliance with the Sarbanes Oxley Act (SOX) and the Financial Industry Regulatory Authority (FINRA). Synthetic test data mimics stock prices, trading volumes, or transaction records for back-testing trading strategies and risk modeling.
Financial institutions like American Express and J.P. Morgan use tabular synthetic data to boost their fraud detection capabilities. By generating statistically accurate synthetic data from financial transactions, these organizations can train and fine-tune fraud detection models without exposing sensitive customer data.
Synthetic Data in Healthcare
Synthetic data in healthcare is transforming the industry, as strict privacy regulations like the US Health Insurance Portability and Accountability Act (HIPAA) often make it difficult to work with sensitive patient information.
Synthetic data can be used to simulate patient records, medical images, or genomic data, which enables data sharing while maintaining confidentiality, and can also serve as a baseline for studies and testing when real patient data is unavailable.
Synthetic Data in Retail and e-Commerce
Synthetic data helps streamline businesses operations and protect customer identities. Retailers employ synthetic data to better forecast demand, personalize customer experience, manage supply chains, and comply with data privacy laws like CPRA, GDPR, and PCI PSS.
Synthetic data creation allows businesses to subset their data more effectively, detect fraud, and train chatbots to be more engaging. By facilitating competitor analysis, dynamic pricing, and visual merchandising more effectively, retailers can innovate and optimize, while always respecting privacy boundaries.
Synthetic Data Examples Based on Business Entities
Enterprises in highly regulated industries are learning how to create synthetic data by business entity (customer, order, or loan), a unique approach which enforces referential integrity across multiple data sources, no matter which synthetic data generation technique is used.
Entity-based synthetic data generation tools integrate a variety of data generation methods, such as:
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Generative AI
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Rules engine
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Entity cloning
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Data masking
Although there are a few good synthetic data companies to choose from, only 1 (K2view) supports all these methods.
