Quality Intelligence
See change before it becomes risk with change impact analysis and risk assurance.
Enterprise technology change introduces risk that most organisations cannot fully see. A release may appear stable, test coverage may look sufficient, and delivery teams may report confidence. Yet the underlying question often remains unanswered: what has actually changed, and what does that mean for the business?
This is where quality risk accumulates, often unnoticed until go-live or shortly after.
Why Testing Alone Leaves Risk Unseen
ERP platforms, cloud applications, and custom-built systems evolve continuously, often across multiple teams and technologies. No single view exists that connects these changes to business processes.
Teams responsible for delivery are also responsible for validation. Under delivery pressure, testing expands, but prioritisation becomes less precise. The incentive to ship and the obligation to assure sit with the same people.
Quality signals are often expressed as pass rates, defect counts, or coverage percentages. These metrics do not answer the question that matters at decision level: what risk remains?
Quality Intelligence: Making the Impact of Change Visible and Actionable
Quality Intelligence addresses a specific problem: understanding the impact of change before it becomes operational risk. It sits upstream of testing, determining where effort should be directed, why it should be directed there, and what remains exposed if it is not. Without it, even a well-resourced Quality Engineering (QE) programme can cover the wrong ground.
With Quality Intelligence tool, testing can be prioritised based on operational and regulatory risk, rather than coverage targets or delivery timelines.
Tricentis LiveCompare provides AI-driven impact analysis for SAP. It compares system versions to identify how a change affects code, configuration, data, and security, then maps those changes to existing test coverage. TTC Global applies it to focus testing on the highest-risk objects and reduce regression scope.
SeaLights provides code-aware quality intelligence for modern and custom applications. It measures test coverage across every test type, identifies code changes that have not been validated, and ties coverage to specific releases. TTC Global uses it to direct testing toward real gaps and confirm what each release has actually tested.
Tricentis LiveCompare provides AI-driven impact analysis for SAP. It compares system versions to identify how a change affects code, configuration, data, and security, then maps those changes to existing test coverage. TTC Global applies it to focus testing on the highest-risk objects and reduce regression scope.
TTC Global’s Differentiated Assurance-Led Approach
Quality Intelligence is applied as part of an Intelligent Quality Engineering model, where validation is directed by risk and governed independently of delivery pressure. TTC Global’s starting point is a structured understanding of change, embedded directly into how release decisions are made. This is achieved through four principles:
Change is made visible across systems, code, and processes, reducing reliance on assumption.
Testing effort is prioritised based on operational and regulatory exposure, not coverage targets.
Gaps between system change and test coverage are identified before release.
Quality signals are structured to support executive-level release decisions.
Delivery teams retain velocity, while governance ensures that speed does not compromise control.
Post-go-live disruption and extended hypercare are reduced through earlier identification of risk.
Where Quality Intelligence Has the Greatest Impact
Quality Intelligence is most valuable where the scale or complexity of change outpaces an organisation's ability to assess its impact manually, and where the consequences of a misjudged release are significant.
- Large-scale ERP transformations, including SAP S/4HANA or Oracle Cloud programmes
- Frequent release cycles where change volume exceeds the ability to assess impact manually
- Programmes involving multiple delivery partners where objective validation is required
- Environments where previous releases have led to unexpected production issues or extended hypercare
- Regulated industries where release decisions must be supported by structured, auditable quality evidence
- Organisations where testing investment is high, but confidence in release outcomes remains uncertain
Frequently Asked Questions about Quality Intelligence
Testing confirms whether a system works as expected. Quality Intelligence determines where testing effort should be directed and what risk remains if it is not. Testing executes against known cases; Quality Intelligence works upstream to identify what has changed, what that change affects, and where coverage gaps exist. The two are complementary: Quality Intelligence directs testing, and testing validates the areas it identifies.
An implementation partner is accountable for delivery, including scope, schedule, and budget. When validation sits inside that structure, the same team is responsible for both shipping and assuring the release, and quality decisions can be shaped by delivery pressure. Independent assurance is governed outside the delivery chain, so change is assessed objectively and release decisions are based on evidence rather than delivery confidence.
Change impact analysis identifies how a specific change to a system affects the rest of it, including business processes, integrations, custom code, configuration, and security. In quality engineering, it is used to determine which areas a change puts at risk and which tests need to run, so testing focuses on the highest-risk areas rather than repeating the full regression suite for every release.
Regression testing time can be reduced by combining change impact analysis with risk-based prioritisation. Impact analysis identifies which business processes and components a change affects, so testing focuses on the highest-risk areas rather than re-running the entire suite. Coverage analysis then confirms which tests are relevant to what changed. Together, these reduce test scope while maintaining confidence in the areas that carry the most operational risk.