Synthetic Data Tools via Data Products are a Win-Win for Enterprises

Learn how a data product approach enables all synthetic data generation methods and use case examples with just 1 set of self-service synthetic data tools. 

Why Enterprises Need to Generate Synthetic Data 

Synthetic data, which is realistic yet fabricated data, serves various purposes such as safeguarding personal privacy, testing software applications before release, training Machine Learning (ML) models, and validating high-scale systems.

The increasing stringency of data privacy and security regulations, along with tightening budgets, have propelled synthetic data generation tools into the spotlight. Another driver is the difficulty in accessing production data when it’s fragmented across many different systems.

Developers require extensive, diverse, and accurately labeled datasets for software testing and ML model training. However, assembling, subsetting, and classifying massive datasets from production sources can be costly, difficult, and unfeasible – and may also risk non-compliance with data privacy laws like GDPR, CPRA, and FIPAA.

Synthetic data generation is the obvious answer, but the resultant fake data must be as complete, accurate, and compliant as possible.

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

Enabling Synthetic Data Tools with Data Products 

A data product is a reusable data asset designed to deliver a reliable dataset for a particular purpose.  

A data product platform integrates data from relevant sources, processes that data, assures its compliancy, and then makes it immediately accessible to authorized users.  

Data products have well-defined interfaces, metadata, and SLAs – making them completely reusable by other teams within the organization.  

With a data product approach to synthetic data tools, data teams can reuse the same data products for various synthetic data examples – accelerating innovation, increasing agility, and reducing costs across the organization. 

Synthetic data tools based on data products should be able to: 

1. Cover all methods of synthetic data generation (as listed in the next section) 

2. Connect to all underlying data sources 

3. Subset the data upon extraction 

4. Mask sensitive data upon discovery – automatically 

5. Reserve, version, and rollback the synthetic datasets, as needed 

6. Integrate with CI/CD pipelines 

Support for the 4 Key Data Generation Methods 

Enterprise synthetic data tools – based on data products – support the 4 main data generation techniques, including: 

1. Generative AI 
   The generative AI synthetic data method, used when not enough production data is available, leverages GPT models to: 
   – Subset the source data needed to train the model 
   – Mask the training data to ensure compliance 
   – Train the GPT model to generate the synthetic data 
   – Apply business rules to increase accuracy 
2. Rules Engine 
   Primarily employed to test new application functionality, the rules engine should be able to: 
   – Generate data based on pre-defined business rules – on demand or via API 
   – Create business entities, such as customers, automatically 
   – Customize, test, and debug functions without coding 
   – Define business rule parameters 
3. Entity Cloning 
   Entity cloning is used for performance and load testing to: 
   – Generate massive datasets on demand 
   – Select the most relevant business entity (e.g., a customer with the right criteria for a particular test case) 
   – Extract, mask, and clone the entity along with all its data 
   – Create unique identifiers for every cloned entity 
4. Data Masking 
   Facilitated by data products, the data masking technique is unique its ability to: 
   – Anonymize sensitive data in a very lifelike way 
   – Discover Personally Identifiable Information (PII) automatically 
   – Customize data masking functions 
   – Mask data inflight, as it’s extracted from the underlying source systems 

Only synthetic data tools based on data products support all 4 data generation methods. 

Synthetic Data Lifecycle Management Enabler 

Synthetic data tools based on data products provide end-to-end synthetic data lifecycle management – from data extraction, through generation, to pipelining and operations.  

In summary, they are uniquely qualified to: 

• Provision compliant data subsets without any coding 
• Mask PII and sensitive data on the fly 
• Reserve data subsets for specific users 
• Version and roll back datasets on demand 
• Integrate data into CI/CD and ML pipelines via APIs 

Essentially, the data-as-a-product principle enables synthetic data tools to perform at enterprise-grade speed, scale, and security levels. 

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