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Data Validation in 2 Minutes
Uncover and “test” relationships in your data, while defining valid and invalid data combinations. Data validation from Test Data Automation provides the deep system understanding needed to test and develop at speed, while avoiding the catastrophic bugs caused when bad data combinations make it to production.
Poorly understood systems and “bad” data cause critical bugs
Too many bugs and bottlenecks arise from a lack of understanding regarding complex systems and the data that fuels them. Without robust documentation, developers trawl through code and cast ad hoc SQL queries. They cannot map complex relationships, nor understand which data will be updated by their changes. Developers instead create redundant tables and bloated designs, while bugs mount due to unforeseen effects of system change. Invalid data further makes it to production, causing more bugs. Testing cannot find the defects early enough, as they lack deep understanding of valid and invalid combinations, while generating test data is impossible without understanding the data model. Testers and developers today need greater understanding of complex data, and "good" and "bad" data to test with.
Understand your data - stop invalid combinations reaching production
Data validation automatically analyses complex data, uncovering new relationships and defining which data combinations should and should not occur together. Developers can design optimal schemas and avoid costly production bugs, understanding which interrelated data their changes will impact. Testers can further test complex systems rigorously to avoid “bad” data in production, understanding invalid combinations and generating “covered” data for rigorous testing. Including validation in DevOps pipelines further safeguards production, blocking bad data from slipping past testing and development. Data validation provides BAs and engineers with the understanding they need to pay off technical debt and design better requirements, better code, and better tests.
Understanding complex data relationships further supports legislative compliance, reducing the risk of devastating fines and brand damage. You can locate and categorise sensitive data, understanding what data is being used, and for which purposes. You can then find, copy and erase interrelated data on demand, complying with the Rights to Erasure and Data Portability. Understanding complex data models and relationships further allows you to generate accurate synthetic data for complex systems. The on demand data generation from Test Data Automation boosts test coverage and quality, while sidestepping complex compliance requirements and delivering quality systems faster.
Automated data validation
Watch the two-minute overview of Data Validation from Test Data Automation, to see how:
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Data validation from Test Data Automation automatically analyses complex data to identify and verify relationships, providing the understanding needed to develop at speed.
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Testers and developers can design optimal database schemas, avoiding the proliferation of redundant tables and reflecting all the requisite data relationships.
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Validation identifies “good” and “bad” data combinations, generating “covered” test data sets to ensure that production systems effectively handle bad data and avoid devastating bugs.
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Including validation in your DevOps pipeline further safeguards production systems, stopping invalid combinations from sneaking past testing and development.
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Data validation supports complex compliance requirements, allowing you to categorise interrelated data, before finding, erasing and copying sensitive data on demand.
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Understanding the logical data model of your data enables synthetic test data generation, boosting coverage, sidestepping compliance requirements, and accelerating delivery.
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Data validation is quick and easy to set up in Test Data Automation’s central portal, which automatically analyses data relationships and looks for erroneous data that does not fulfil them.
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You can use Test Data Automation to add in new foreign key relationships to create a complete database, or can generate a report to share with your DBAs.
Speak with an expert
Understand your data and identify ‘bad’ data to avoid it in production.