What does this do?
Security and data quality helps make the security and data quality workflow decision-ready in Testvoy. The goal is not only finding a setting, but understanding why it exists, how to read the outcome and where to debug first.
Know when to use it
Learn which UI fields matter
Know what success looks like
When should you use it?
Use this guide during first setup, experiment creation, report interpretation or before sharing with a client or team. Testvoy keeps the workflow approachable for non-technical users while still exposing SDK, event and security context for developers.
- 01
Choose the relevant workspace and project
- 02
Follow the checklist on this page
- 03
Verify with preview or verifier
- 04
Share the setting or report with the team
How to do it
Most Testvoy workflows follow the same pattern: choose context, define the rule or change, then verify through QA and reporting. This keeps the product focused on decision quality, not only running tests.
- Is the workspace correct?
- Is the project key or domain correct?
- Does the goal/event match?
- Were mobile and desktop checked?
- Any bot/SRM or dropped event warning?
Example data-quality check
Example: a report shows unexpectedly high conversion. Before deciding, review bot signals, signed event context, dropped event reasons, rate-limit signals and SRM checks in order.
- 01
Open Report > Data quality
- 02
Check suspicious traffic count
- 03
Look for signature/context errors in dropped events
- 04
Inspect rate-limit or abuse guard signals
- 05
If SRM warning exists, hold decision or re-QA
- 06
Share the clean report with a shareable link
Traffic quality: healthy
SRM: no mismatch
Dropped events: 0.8% invalid context
Suspicious traffic: reviewed separately
Decision status: safe to reviewPractical scenario
A growth team changes CTA copy on the pricing page, connects signup_started and signup_completed goals, then reads Google Ads and returning visitor segments separately. If the result is promising, they share a report link with the client or leadership.
Common mistakes
Most mistakes come from wrong project keys, overly broad selectors, missing goals, staging/prod mixups, early decisions on small samples or skipping mobile QA.
Naming an experiment 'test'
Launching without a goal
Deciding on total CVR only
Sending screenshots instead of shareable reports