How to Create Synthetic Data

Learn how to create synthetic data, which mimics the structure and characteristics of real-life data to protect Personally Identifiable Information (PII).   

Table of Contents

What is Synthetic Data?
Why Enterprises Need to Learn How to Create Synthetic Data  
How to Create Synthetic Data in 4 Different Ways 
Synthetic Data Creation Challenges
How to Create Synthetic Data More Effectively

What is Synthetic Data?  

Synthetic data is imitation data – created to closely resemble actual real-world data in structure, format, and other mathematical and statistical characteristics. A synthetic dataset is created to simulate actual data that may contain sensitive information like PII, or production and operational data. Today's businesses are actively figuring out how to create synthetic data to avoid the regulatory limitations and potential legal liability associated with working with personal or sensitive data. 

Why Enterprises Need to Learn How to Create Synthetic Data  

Companies need large, diverse, and accurately labelled datasets to test applications under development and train Artificial Intelligence/Machine Learning (AI/ML) models. However, gathering and labelling these datasets is time consuming and expensive. What’s more, it may be technically impossible, or in breach of data privacy laws, to gather and use such datasets from production. Thus, organizations need  synthetic data solutions in order to: 

• Protect privacy 
  Firms that handle PII are legally obligated to effectively safeguard it under regulations like GDPR, CPRA, HIPAA and others. To allow secure testing and analysis, and avoid breaches of privacy, it’s possible to synthesize data to look and feel like the real deal, yet contains no actual personal details. 

• Augment existing datasets 
  Building and fine-tuning AI/ML models require diverse and representative data. Enterprises that generate fake data can augment existing data set to ensure

  
  

  effective model development and validation. 

• Overcome data scarcity 
  In some cases, such as when testing new software functionality or when engaged in negative testing, real data is insufficient or missing. In such cases, companies can synthesize data, while also supporting research, development, and decision-making. 

• Anonymize sensitive data 
  When organizations need to share data with third parties, or collaborate with peers, they can create synthetic data to mask confidential information in the data they share. Data masking ensures data privacy, while still enabling the benefits of collaboration for research or other purposes. 

• Lower testing overheads 
  The acquisition and maintenance of large-scale real datasets can be prohibitively expensive and resource intensive. Companies can create synthetic test data at a fraction of the cost – conducting testing and research with far lower financial and resource commitments. 

How to Create Synthetic Data More Effectively 

There are 4 primary methods used to address a wide range of synthetic data examples:

1. Generative AI
   Generative AI synthetic data techniques leverage ML models, such as Generative Pre-trained Transformer (GPT), Generative Adversarial Networks (GANs), and Variational Auto-Encoders (VAEs). The models "learn" from production data to fabricate synthetic data that's strikingly similar to the real thing.
2. Rules engine
   A rules engine generates data using pre-defined business policies. Data teams can add intelligence to the generated synthetic dataset by referencing the relationships between the various data objects, to assure relational integrity across all systems.
3. Entity cloning
   Entity cloning extracts the data for a chosen business entity (e.g., a specific customer) from underlying sources, masking and cloning it inflight. It's ideal for quickly creating the huge amounts of data necessary for performance or load  testing, because unique identifiers are generated for each cloned entity.
4. Data masking
   Data masking protects sensitive or personal information, while retaining the characteristics and statistical properties of the real data. It substitutes pseudonyms or altered values for confidential information, while preserving data utility.

Synthetic Data Creation Challenges 

Creating accurate synthetic data is challenging due to the need to represent complex real-life distributions, preserve privacy, balance datasets, and scale to high volumes, as detailed below: 

1. Representation 
   When generating synthetic data, it’s crucial to capture the full range of patterns and variations from the original dataset. When these datasets are complex and have high dimensionality, accurately replicating the intricate relationships between variables can be a significant challenge. 

2. Privacy preservation 
   Even though creating artificial data can alleviate the challenges of using highly-sensitive actual data (for example, generating synthetic patient data instead of using real medical information), striking a balance between data utility and privacy protection remains a challenge in and of itself.

3. Imbalanced datasets 
   When generating synthetic data, it’s crucial to ensure the transferability of the synthetic dataset to real-world scenarios. This challenge needs to be overcome to ensure that models trained on synthetic data perform well on real data, too. 

4. Scalability 
   Creating synthetic data is compute intensive, especially for complex generative models or large and complex datasets. Creating enough synthetic data at scale may present challenges to organizations lacking sufficient computing resources.  

How to Create Synthetic Data More Effectively 

Synthetic data can be created more efficiently and cost-effectively using synthetic data generation tools based on a business entity approach. The entity-based model enables the creation of highly realistic synthetic data, while still enforcing referential integrity. This ensures that data generated for a given task is both relevant and contextually precise.  

Entity-based synthetic data generation uses a variety of data generation techniques, alone or together, based on: 

1. Generative AI, which leverages machine learning models to create realistic tabular data 

2. Rules engine, which creates synthetic data based on various rules and statistical distributions applied to the model 

3. Entity cloning, which duplicates a single entity with all its related data, while changing the identifiers in each clone 

4. Data masking, which can anonymize data at the entity level, to ensure that the source data seems like realistic synthetic data

Only entity-based tools can generate fake data based on all of these techniques.