In a digital ecosystem where every click generates information, data quality becomes an essential component of quality assurance. During the latest SII Tech Talks session, our colleague Verónica Sofía Batista shared a practical perspective on a topic that is gaining increasing importance in testing teams: analytics applied to QA.
Beyond bugs: validating data quality
The role of QA is no longer limited to finding visible errors in an interface. In the era of advanced analytics, the value of testing also lies in ensuring that the data flowing from a website or app to platforms such as Google Analytics 4, Adobe Analytics, or BigQuery is consistent, accurate, and useful for decision-making.
Verónica Sofía Batista explained how testers are facing new challenges: validating “invisible” data for the user, tracking the full traceability of an event from the click to the dashboard, and ensuring consistency across development, pre-production, and production environments. This is a process where coherence and accuracy are just as critical as user experience.
From analytics to knowledge
The presentation highlighted a key principle: data only has value if it is transformed into knowledge.
In QA Analytics, this journey begins with a clear tagging plan—defining what is measured and why—and continues with rigorous technical validation. Tools such as Google Tag Manager, Tag Assistant, Charles Proxy, or Analytics Debugger help ensure that events and parameters are correctly captured before reaching analytics platforms.
The goal is not just to measure, but to measure well: ensuring that business metrics (conversions, engagement, retention) truly reflect the technical reality of the product.
The new QA role: guardian of digital trust
The talk concluded with a powerful reflection: in an environment where strategic decisions are driven by data, QA becomes the guardian of analytical reliability. Ensuring data quality is also ensuring the quality of decisions. And in organizations where analytics drives growth, analytical testing is not an add-on—it is a pillar.
The combination of QA and analytics marks a natural evolution in companies’ digital maturity. Validating data with the same rigor as code strengthens traceability, transparency, and trust in systems. As Verónica put it, “analytics is not just a measurement tool, it is an ally in continuous improvement.”
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