#data quality
5 pieces filed under “data quality”.
Programmatic SEO without producing landfill
The workshop version of programmatic SEO: dataset maturity gates, the four page archetypes that work, editorial review thresholds, and the build sequence we trust.
Writing an analytics implementation plan before you tag anything
A measurement plan decides what to track; an implementation plan decides how. Naming, property hygiene, consent mode, staging QA and the 30-day calibration.
Analytics pipelines you can actually trust
Dashboards nobody trusts get quietly ignored. Event schemas, validation at the edge, QA environments for data, and pruning metrics nobody has read in two quarters.
Name your events before you buy the analytics tool
Event naming decides whether your data answers questions in year three. The taxonomy anatomy we design before any tool evaluation: verbs, properties, screens, migration.
Analytics governance: tracking plans before tools
Analytics rots quietly until nobody trusts the dashboards. The fix is governance, not tooling: event naming grammar, ownership, QA rituals and the monthly data hour.