Data trading markets that span industries and regions are expanding, but the implicit trust that supports data use within an organization does not carry over to cross-domain exchange. This paper examines the Data Credit Guarantee Organization (DCGO), a proposal to institutionalize trust through third-party verification and certification, and asks what it means to trust data, what such certification can support, and under what conditions DCGO outperforms existing trading models. We decompose trust in data into four layers: authenticity of entity, authenticity and integrity of data, rights and conditions of use, and history and auditability. A third party can certify authenticity but not the correctness of content, and DCGO is positioned as a trust anchor spanning existing marketplaces and data spaces. We then extend the Law Merchant model with three data-specific properties, namely imperfect ex-post detection, cheap pseudonyms, and non-rivalry of harm, and compare bilateral trading with DCGO by agent-based simulation. DCGO keeps the buyer's payoff near its theoretical upper bound regardless of the market environment, whereas bilateral trading loses even when fraud is always detected. Identity verification proves to be a prerequisite for record sharing, and pre-verification is the only function that limits the spread of harm. On the seller side, honest sellers join voluntarily whenever the price premium exceeds the fee. Lock-in and a critical mass for diffusion, however, call for fee governance and support for entry.

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