Structured Data Audit: Valid Markup, Useful Meaning
Check whether markup accurately represents visible content, eligible entities and the relationships a search system needs.
Inventory markup by template
Check whether markup accurately represents visible content, eligible entities and the relationships a search system needs.
State the scope, known limitations and the decision the work must support before collecting more data.
Compare properties with visible facts
Use representative pages, queries, campaigns and customer journeys. Compare observed evidence with what the business assumes is happening.
Record sources, dates and filters so another person can reproduce the finding.
Resolve conflicts and unsupported claims
Turn the evidence into a short sequence of changes with an owner, acceptance check and a clear reason for the priority.
Protect access, measurement and high-value customer actions before lower-impact refinements.
Monitor eligibility after release
Review leading indicators together with qualified commercial outcomes. Annotate releases and avoid claiming causality from one movement.
Keep, revise or stop the work according to evidence and customer feedback.
What clients usually want to know.
What should be checked first?
Start with the customer action and the evidence closest to revenue, then work backwards through the journey.
How much data is enough?
Enough to represent normal conditions and expose meaningful patterns; document uncertainty when the sample is limited.
How should success be judged?
Use implementation checks, leading behaviour and qualified commercial outcomes rather than one isolated metric.