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All methods
of data generation
Supporting the 4 key synthetic
data generation techniques
Any use case
with 1 set of tools
Testing apps, training AI/ML models, sharing B2B data, and much more
Self-service
portal and APIs
Empowering data teams with
full control and automation
- 01 Generative AI
- 02 Rules Engine
- 03 Entity Cloning
- 04 Data Masking
01Generative AI
Generative AI is used when there's not enough production data 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
02Rules Engine
Rules engines, used for testing new application functionality, must:
- 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
03Entity 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
04Data Masking
Data masking, which obscures sensitive data, must:
- 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
Auto-discover the business entity schemas (e.g., customer, device, loan, order, etc.) for which the synthetic data is needed.
Apply the appropriate data generation method(s) to the data model, to create the most complete, accurate, and compliant synthetic data possible.
Deliver the data to the target systems and manage access, reservation, versioning, rollback, and integration with CI/CD and ML pipelines.
INTERACTIVE PRODUCT TOUR
Synthetic Data Generation
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Synthetic Data Generation
With K2view, dev and QA teams can generate accurate and compliant synthetic data for their testing needs.
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Next flow: Data subsetting
Data subsetting
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Synthetic Data Generation
With K2view, dev and QA teams can generate accurate and compliant synthetic data for their testing needs.
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Next flow: Data Masking
Data Masking
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Synthetic Data Generation
With K2view, dev and QA teams can generate accurate and compliant synthetic data for their testing needs.
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Next flow: Time machine
Time machine
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Synthetic Data Generation
With K2view, dev and QA teams can generate accurate and compliant synthetic data for their testing needs.
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