The deepening of industry-finance data sharing has fostered a new paradigm of platform governance underpinned by trust mechanisms and transparent pricing, which is crucial for activating the market for data as a factor of production. Given the dual role of banks as both market participants and platform governors within the data-sharing ecosystem, this paper proposes a "bank-chain-leader" architecture for an industry-finance data-sharing platform. Based on this framework, we develop a bank-led, signal?state?dependent option pricing model to systematically characterize the optimal pricing mechanism for banks valuing industrial data. Our findings indicate that under the implicit transmission of signals, an enhanced individual informativeness effect prompts banks to lower the option's strike price and increase the vesting ratio. Regarding the location and precision effects, if the signal indicates that the enhanced marginal explanatory power of data stems from its endogenous value, banks should lower the strike price and increase the vesting ratio; conversely, if it arises predominantly from external environment improvements, the opposite strategy should be adopted. Furthermore, a bank's initial data endowment and data scale both exhibit a U-shaped relationship with the optimal strike price and an inverted U-shaped relationship with the optimal vesting ratio. There exists a structural compatibility range for the proportion of industrial credit?quality data; within this range, both pricing metrics decrease monotonically as the proportion rises, whereas they increase outside of it. Moreover, when multi-dimensional external signals are explicitly embedded into the pricing system, the adjustment to the strike price depends on the relative dominance of the location versus precision effects, whereas the vesting ratio adjustment is primarily driven by the precision effect. Finally, the dominant nodes of these two effects and the critical threshold for the precision effect are dynamically modulated by exogenous variables, including macroeconomic cycles, heterogeneity of bank data endowments under varying accounting performance, and sensitivity to sustainability thresholds.