Tariff shocks force firms to make decisions about sourcing, inventory, logistics, and supplier changes. Analytical models can support these decisions only if the information they use is clearly defined and measured consistently. This paper examines that data-readiness problem. Based on recurring tariff-response mechanisms identified in prior research, 12 candidate decision inputs rated by 11 experts are organized into four layers: policy state, exposure topology, substitution feasibility, and execution feasibility. Measurement maturity combines expert ratings of input clarity and the suitability of operational rating anchors, while relevance captures decision importance. Exposure topology shows the highest maturity at 0.851, policy state the largest gap between importance and maturity at 0.059, and execution feasibility the lowest maturity at 0.773. The findings identify where stronger data preparation is required before modeling. The paper proposes a gated data architecture that converts tariff information into auditable inputs for simulation, optimization, and stress testing.