Test data generation seamlessly integrates with rapid data cloning, providing all the volumes of unique data combinations needed by parallel test teams and tests. This adds to provisioning delays while inconsistencies across components lead to frustrating test failures. Manual test data processes struggle to fulfil relationships within and across data sources. Inconsistent test data creates frustrating test failures: This passes variables from one process to the next, generating consistent data journeys for testing. On demand data journeys test complex systems in-sprint:Įasy-to-use functions, side-by-side configuration and visual data flows publish data into multiple targets at once. Data becomes increasingly out-dated relative to systems under test, creating test failures. Each job becomes re-usable in a central catalogue, breaking dependencies on a siloed team.ĭata refreshes fall behind rapid releases:Īn over-worked data provisioning team struggle to reflect highly complex data relationships and are quickly overwhelmed by repetitive requests. Test Data Automation models data structures, using fill-in-the-blanks configuration and visual data flows to generate referentially intact data. Rapid and repeatable test data generation: They must then through large data sets for the combinations they need. Test and development teams face lengthy waits for data provisioning, as a central team struggles to fulfil complex data dependencies. Teams can trigger publishes from an online portal, delivering fully tested software in-sprint. “Just in time” generation and allocation find and make data as tests are generated or run. PII in test environments risks costly non-compliance:Ĭopies of production contain personally identifiable information and commercially sensitive data, risking costly non-compliance with data privacy legislation, significant brand damage and customer churn. Combining generation seamlessly with masking provides a hybrid approach to compliance. Synthetic data provides all the combinations needed for testing, but with none of the sensitive content. Realistic but fictitious test data minimises risk: Production data contains a fraction of the combinations needed for rigorous testing, lacking the outliers and unexpected results needed to prevent bugs hitting production Rich synthetic test data fills gaps in coverage, applying coverage analysis, 200+ generation functions and flow-based generation to find bugs earlier and at less cost to fix.
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