Data Testing
Trust the data behind your critical systems and decisions.
Data powers business decisions, customer experiences, reporting, automation and AI.
But as it moves between systems, changes through transformations and flows through increasingly complex environments, traditional application testing doesn’t always tell you whether the underlying data is accurate, complete and fit for purpose.
TTC’s Data Testing services help organisations reduce risk across:
- Data migrations - validate what moved and what changed
- Business rules and reporting - confirm the logic behind critical outcomes
- Data pipelines and automation - test the quality of what flows through
- AI data and outputs - define, benchmark and validate quality
By combining specialist data expertise with testing expertise, TTC helps focus assurance where it creates the greatest business value.
Working doesn’t always mean right
Data problems can sit quietly underneath systems that appear to be working perfectly
A data issue doesn’t always trigger an error message or bring a system down.
It can look like:
- A migration completing successfully while records are missing or incorrectly transformed
- A dashboard displaying the wrong result because the underlying business rule is flawed
- A pipeline continuing to run while inconsistent data flows downstream
- An AI solution producing convincing outputs that don’t meet the required quality standard
- Teams relying on manual workarounds because they no longer trust official reporting
TTC helps organisations move beyond “did it work?” to answer the more important question: Can we trust the result?
Our Data Testing Services
Data Migration Testing & Assurance
Know that the right data moved, changed correctly and still means what the business expects
A migration isn’t successful just because the transfer completed.
Mappings, transformations, incremental updates and downstream integrations can all introduce issues that may not become obvious until after go-live.
TTC helps identify:
- Missing or duplicated records
- Source-to-target discrepancies
- Incorrect transformations
- Mapping errors
- Delta and incremental-update issues
- Downstream impacts
- Gaps in migration coverage
The result is clearer evidence to support migration and cutover decisions - while focusing testing effort on the areas that matter most.
Business Rules & Reporting Testing
Make sure the system is delivering the outcome the business actually intended
A technically correct system can still produce the wrong business result.
TTC validates the rules, calculations and reporting logic behind critical outcomes, including unusual scenarios and boundary conditions that standard test data can miss.
We help validate:
- Business rules and calculations
- Reporting logic
- Regulatory requirements
- Boundary and anomalous scenarios
- Complex combinations of data
- Alignment between business intent and technical implementation
Data Pipeline & Automation Testing
Test what’s flowing through the pipeline not just whether the pipeline is running.
A pipeline that stops is usually easy to find.
The harder problem is one that keeps operating while incomplete, inconsistent or corrupted data quietly moves downstream.
TTC helps test:
- Data transformations
- Pipeline outputs
- Boundary and anomalous data
- Data consistency between stages
- Failure and exception scenarios
- Monitoring and observability
This helps organisations identify issues before unreliable data reaches the systems, reports and decisions that depend on it.
AI Data Testing & Benchmarking
Define what “good” looks like when there isn’t always one correct answer
AI introduces different quality challenges from conventional software.
Outputs can vary between runs, expected outcomes may be difficult to define, and systems can fail in ways traditional testing approaches weren’t designed to uncover.
TTC helps organisations:
- Define meaningful measures of AI quality
- Develop benchmarking approaches
- Validate the data used to test AI
- Test unusual and adversarial scenarios
- Identify unreliable or inconsistent outputs
- Build evidence around whether an AI solution is performing as intended
The aim is not simply to test whether AI produces an answer but to establish whether the answer meets an appropriate standard for the use case.
Why TTC?
We're data specialists who know how to test, and testing specialists who understand data.
Data Testing shouldn’t be another checkbox in a project plan.
TTC brings together specialist data and testing capability to understand:
- How data is built
- How it moves and changes
- What the business expects it to mean
- Where it is most likely to fail
- What evidence will actually provide meaningful assurance
Our approach is deliberately tailored to the technology, business context and level of risk.
Sometimes that means building a sophisticated reusable framework.
Sometimes it means finding a focused solution that removes a bottleneck or provides the evidence needed to make a decision quickly.
The goal isn’t more testing. It’s better assurance. And more testing doesn’t automatically mean more assurance.
We focus effort where it makes the biggest difference.
Trying to test everything can consume significant time and budget without necessarily giving decision-makers better evidence.
TTC focuses testing effort on the risks and validations that matter most.
That could mean:
- Validating a critical migration before cutover
- Testing a high-risk regulatory rule
- Investigating a pipeline that appears to be working
- Improving inadequate test data
- Establishing meaningful benchmarks for an AI solution
We combine specialist data expertise with testing expertise to focus assurance where it creates the greatest business value.
Proven across complex data challenges
