Data Asset Quality Evaluation Model Considering the Requirements of Circulation Scenarios

High-quality datasets are increasingly recognized as foundational inputs to economic development, industrial upgrading, and public governance. A rigorous evaluation system for data asset quality is therefore needed to improve data governance and to enable value realization in circulation. Focusing on three representative circulation scenarios—data interaction, data exchange, and data trading—this study develops an indicator system from technical, business, and benefit-oriented dimensions. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to identify causal relationships among indicators and key drivers. To integrate multi-expert judgments under uncertainty, hesitant linguistic variables and evidence theory are adopted, and the Best–Worst Method (BWM) is applied to derive more consistent indicator weights. The resulting weights are combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to obtain a comprehensive ranking of data asset quality with scenario-adjustable emphasis. A traffic-flow dataset from a data technology enterprise is used to demonstrate applicability and effectiveness. The proposed framework advances scenario-adaptive data quality evaluation and supports enterprise data governance, data transaction pricing, and the implementation of high-quality dataset policies.