Synthetic Data Generation Tools | K2View

Business entity approach

Enforce referential integrity of the generated data by applying a data model blueprint.

4 data generation techniques

Control how test data is created using any combination of synthetic data generation methods.

Self-service portal or APIs

Empower teams to generate and integrate synthetic data into CI/CD and ML pipelines.

K2view synthetic data generation is a one-stop solution for preparing, generating, and managing compliant, realistic, and accurate data for software testing and ML model training. It generates synthetic data by business entities (such as customer, device, order, etc.), ensuring that all the required data for each business entity is consistent and contextually accurate.

The business entity model serves as the blueprint for generating synthetic data, regardless of the method. It automatically discovers and classifies data formats, structures, and types – while enforcing referential integrity across all source systems.

01 Generative AI
02 Rules engine
03 Entity cloning
04 Data masking

01Generative AI

For data augmentation

Subset the data needed for model training.
Mask training data to ensure compliance.
Train the GPT model based on masked data subsets.
Generate the synthetic data.
Apply business rules for increased accuracy.

Synthetic data generation tools - generative AI

02Rules engine

For functionality/negative testing

Automatically create entity model data generation functions.
Customize, test, and debug the functions, code free.
Empower users to set up the business parameters.
Generate data on demand or via API.

Synthetic data generation tools - rules engine

03Entity cloning

For performance/load testing

Instantly generate large datasets.
Extract, mask, and clone a single business entity and all its data.
Create unique identifiers for each cloned entity.

Synthetic data generation tools - data cloning

04Data masking

For all data protection use cases

Auto-discover Personally Identifiable Information (PII).
Apply prebuilt, customizable data masking functions.
Mask data inflight, as it's extracted from the sources.

Synthetic data generation tools - data masking

Generative AI

for augmentation when data is scarce

Rules engine

for functionality and negative testing

Entity cloning

for performance and load testing

Data masking

for all data protection use cases

Auto-discovery

of all data types, sources, and formats

Entity modeling

for a blueprint of the data to be generated

Data subsetting

directly from the source systems

Auto-delivery

directly into target systems