Sii Poland
SII UKRAINE
SII SWEDEN
Sii Poland
SII UKRAINE
SII SWEDEN
Sii implements the Synthesized platform to enable automated, regulator-compliant preparation of test data. The solution removes manual processes and supports Continuous Testing in DevOps environments.
We implement a Data-as-Code approach where data sets are defined, versioned and automatically recreated based on rules and policies. As a result, tests can run on data prepared for specific scenarios – without manual database refreshes – reducing test start-up time and operational workload.
Synthesized uses AI to reproduce data structures, relationships and business logic without using real user information. This gives teams consistent, production-like test data, improving scenario coverage – including edge cases – and increasing overall test effectiveness.
We create referentially consistent subsets of test data that can be prepared quickly and on demand. This enables fast spin-up of test environments while reducing infrastructure costs. It is especially important in cloud and hybrid setups, where smaller, stable data sets accelerate testing and simplify the maintenance of multiple environments.
We implement data masking so that sensitive information is effectively protected while preserving data formats, consistency and logic. This allows teams to safely test integration and end-to-end scenarios and meet regulatory requirements – without manual operations on databases.
We standardise validation rules, consistency and test data quality so tests reveal real code issues – not data-driven noise. Standardised data preparation improves repeatability across sprints and stabilises the testing process.
We provide controlled access to test data, enabling secure collaboration between development teams, QA and external partners. Structured access and compliance policies allow you to scale testing and environments without increasing regulatory risk.
The scope may include:
We have one of the largest QA teams in Poland and experience in projects that deliver measurable improvements in testing – also in the area of test data. That’s why we can connect the tool with the process, not just deploy a platform.
From diagnosis and process design, through tool implementation, to automation and stabilisation across the organisation. We also deliver integrations and standardise ways of working so the solution is maintainable and ready to scale.
Our partnership with Synthesized gives us access to state-of-the-art tools and expertise in test data management, synthetic data generation and automation. This enables us to implement a Data-as-Code approach faster and more safely – using AI mechanisms – and deliver solutions aligned with your architecture, security requirements and compliance standards.
See how we do it, step by step
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In practice, test data management means you stop “chasing data” before every test. You organize where data comes from, how it’s prepared, and who can use it – so test data is delivered repeatably and safely, whenever software testing needs it.
Synthesized helps you automate the work that usually drains QA and engineering time: preparing datasets, subsetting, data masking, quality checks, and controlled access. In short: fewer manual steps, fewer delays, and more predictable software testing cycles.
No. With Synthesized, data can be created programmatically (Data as Code) or processed so it stays safe and useful for test. A production clone is typically heavy, slow to refresh, and full of sensitive information – which increases compliance risk.
It’s like writing a recipe for data. You define what the dataset should contain, how it should look, and what rules apply – and then you can re-create that test data the same way in every sprint. That’s how test data management stops being a one-off firefight.
AI helps analyze relationships and business logic, and it helps automate how test data is generated and validated. In practice, AI reduces manual work and speeds up data preparation without losing the patterns your tests depend on.
To synthesize data means you create new data that behaves like real data without copying real users’ information. Synthesized test data is safer than taking production data and trying to clean it, and it typically delivers more consistent results for test and software testing.
Yes. Synthesized can create realistic data by recreating dependencies and patterns, while keeping sensitive information out of the dataset. That supports compliance and reduces compliance risk – especially when data must be shared with multiple teams.
Yes. You can automate scenario-focused generation and also build data variations for edge cases that are hard to obtain from production. That often improves test coverage faster than maintaining more full-size environments.
When your databases are large and you need many environments. Subsetting gives you a smaller, referentially consistent dataset, so pipeline runs are faster and cloud costs stay under control. It’s also one of the simplest ways to remove a data bottleneck.
Masking is the general idea; data masking is the controlled technique used to protect sensitive fields while preserving format and relationships. Yes, it can still be needed – especially when you must keep certain structures close to production but still meet compliance requirements.
We combine synthesis, subsetting, and masking with validation rules, so data stays safe but still high-fidelity for real workflows. The goal is simple: the dataset should be trustworthy for software testing, not a “toy database”.
We introduce repeatable validation and consistency checks, so data quality stays stable across refreshes and releases. That reduces false failures and makes software testing results more reliable.
We need basic information about sources, relationships, constraints, and sensitive fields, plus your compliance expectations. Typically, a QA lead, a data engineer, and an environment owner join the initial workshops – so we can define what to automate first.
We integrate data preparation into your pipeline so test data can be provisioned on demand – in a controlled and repeatable way. The outcome is fewer manual tickets, faster runs, and less waiting for environment refreshes.
Yes – the approach is designed for enterprise scale, where data access, compliance risk and repeatability matter most. That’s why organizations such as Deutsche Bank and UBS appear in conversations around modern, automated ways to synthesize and manage test data in software testing.
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