Excellent · Verdica composite score based on three weighted sub-scores from public regulator data.
4 active sources · last successful refresh Oct 5, 2026
source data currentHow this is calculated
Composite = Facility Profile × 35% + Compliance Signals × 40% + Enforcement Actions × 25%, scaled to 300–850.Read full methodology →
Each sub-score starts at a baseline and adjusts based on real regulatory-record signals. Zero-delta rows show what we checked and confirmed clean. “Data quality” indicates how much source data informed the score.
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MICROSOFT - GAINESVILLE DATA CENTER - MNZ03 scores 825 — these providers rank higher on Verdica's composite trust score.
The Verdica score is a composite trust score from 300 to 850, computed from Clean Air, Clean Water, RCRA, and SDWA compliance records maintained by the EPA. A score of 825 places MICROSOFT - GAINESVILLE DATA CENTER - MNZ03 in VA in the excellent (800–850) band. The score reflects publicly available regulator data — it is not a rating from Verdica itself and does not incorporate paid reviews or advertising.
Verdica combines three weighted sub-scores drawn from the EPA. Each sub-score reflects a different dimension of environmental compliance — compliance status, inspection findings, and enforcement history — and is normalized against industry peers before being aggregated to the 300–850 scale.
Verdica pulls public records from the EPA: Clean Air, Clean Water, RCRA, and SDWA compliance records. Data is refreshed on rolling cycles, typically weekly to monthly depending on the source agency's own update schedule. See the "source data current" indicator on the entity page for the last successful refresh.
Common negative signals include significant noncompliance flags, high-priority violations, or formal enforcement. Any of these appear in the underlying regulator records and pull the sub-score down; the composite reflects the weighted impact.
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Yes. Entities can file a dispute directly from the score page, and business owners can claim their profile. Verdica reviews disputes against the underlying regulator record; the score is updated when the source data is corrected. Scores are recomputed automatically as new regulator data arrives.