K2view Synthetic Data Generation

K2view's Synthetic Data Generation is part of its Data Product Platform and targets enterprise dev and QA teams that need realistic, compliant data in lower environments. It blends several generation methods in one product and provisions whole business entities across connected systems.

What It Is

The tool generates synthetic data for testing, analytics, and AI. K2view says it picks and runs the right generation method for each use case. The methods are AI generation (learned from masked production data), rules-based generation (from defined business rules, without production data), data cloning (replicating complete business entities at scale), and intelligent data masking (replacing sensitive values with realistic ones while preserving relationships).

How the Workflow Works

Generation is entity-based rather than column-based. An entity data model, covering things like customers, accounts, and orders, serves as the blueprint, so relationships and referential integrity are kept across systems. The vendor describes a data agent that reads test cases, determines the test data needed for coverage, selects and configures generation methods, runs them, and provisions the resulting synthetic entity data to the downstream environment. The page also describes data subsetting in business terms and in-flight masking, plus preparation, masking, provisioning, and refresh without a separate ETL tool.

Compliance and Related Capabilities

The vendor states the generated data can meet GDPR, CCPA, HIPAA, and other regulations. It lists related capabilities in Agentic TDM (self-service portal, subset, reserve, rollback, and aging of test data) and Enterprise Data Masking (PII discovery and classification, static and in-flight masking, customizable masking functions). An interactive product tour and live demo are offered.

Recognition Claims

The vendor-published page cites analyst items, including a QKS Group 2026 SPARK Matrix Leader listing in data masking, tokenization and synthetic data.