This study focuses on the challenge of balancing data development and compliant circulation in the digital economy era. In response to the gaps in existing research on the connotation of data business governance, the impact path of strategies on performance, and participant behavior interaction, an interpretable enterprise performance prediction model based on multi-source operational data is constructed. Adopting typological analysis, econometric analysis, and behavioral empirical analysis, following the technical route of "problem posing theoretical construction empirical testing application implementation". The results showed that enterprise data business governance needs to take into account both data quality and data compliance dimensions, covering first, second, and third level governance dimensions (such as data intrinsic quality governance, data infringement risk, etc.). Strategies are divided into data quality governance (asset management, supply chain management) and data compliance governance (compliance management), and different business types (providers, transformers, etc.) have different forms of strategies; Governance strategies enhance performance through synergistic effects - data asset management directly/indirectly promotes financial performance, supply chain management enhances the role of resource scale, compliance management strengthens innovation impact, and platform complementarity, user scale, and compliance services enhance platform performance through paths such as improving perceived revenue/value, influencing revenue and value, and strengthening fairness; The strategy needs to match the technical, market, and regulatory resource needs of participants to drive value appreciation and monetization. The final model enriches the research on data governance at the theoretical level, expands the resource-based view, and provides guidance for the healthy development of data element markets, enterprise value realization, and the construction of ecological data trading platforms at the practical level.

Niming Wang. Construction and Application of Interpretable Enterprise Performance Prediction Model Based on Multi-Source Operational Data. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 117-126. https://doi.org/10.38007/IJBMET.2026.070114.

[1] Ukezeki Y, Inoue S, Fujimura N, et al. Data processing capability of polarization dynamics in ferroelectric-gate transistor-based physical reservoir computing[J]. APPLIED PHYSICS EXPRESS, 2025, 18(8):081001.

[2] Marucci A. A Typological Analysis Method for Rural Dwellings: Architectural Features, Historical Transformations, and Landscape Integration: The Case of "Capo Due Rami", Italy[J]. Land, 2025, 14. DOI:10. 3390/land14020374.

[3] Hong, Y. (2025). Architecture Design and Performance Optimization of a Large-scale Online Simulation Platform for Business Decision-making. Advances in Computer and Communication, 6(4).

[4] Ye, J. (2025). Application of Nerve Signal Tracking Technology in Rehabilitation of Spinal Cord Injury. Artificial Intelligence and Digital Technology, 2(1), 171-177.

[5] Truong, T. H. (2025). Research on the Application of Digital Healthcare Platforms in Chronic Disease Management. Advances in Computer and Communication, 6(5).

[6] Ye, J. (2025). Optimization and Application of Gesture Classification Algorithm Based on EMG. Journal of Computer, Signal, and System Research, 2(5), 41-47.

[7] Yin, J. (2026). Research on a CLO Secondary Market Spread Volatility Prediction Model Based on RoBERTa Sentiment Factors. Advances in Computer and Communication, 7(1).

[8] Liu, H. (2025). Research on the Evaluation of User Safety Intervention Measures Based on Causal Inference. Engineering Advances, 5(4).

[9] Wang, B. (2025). Strategies and Practices for Load Test Optimization in Distributed Systems. SCIENTIFIC JOURNAL OF TECHNOLOGY Учредители: Boya Century Publishing, 7(2), 132-137.

[10] Wang, B. (2025). Application of Efficient Load Test Strategies in Infrastructure. Journal of Computer, Signal, and System Research, 2(4), 69-75.

[11] Qi, Y. (2026). The AI optimization path for payment gateway operations in the Global Financial Market. Financial Economics Insights, 3(1), 67-73.

[12] Wu, W. (2025, June). Construction and optimization of intelligent gateway software management platform based on jenkins cluster management under cloud edge integration architecture in industrial internet of things. In International Conference on 6G Communications Networking and Signal Processing (pp. 633-645). Singapore: Springer Nature Singapore.

[13] Wu, L. (2025, December). Design and Application of Automatic Data Set Generation Tool Based on KLEE in Embedded Memory Management Performance Test Framework. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1111-1117). IEEE.

[14] Qi, Y. (2025, October). Research on Privacy Protection of AI Models in Big Data Using Differential Privacy Technology. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-5). IEEE.

[15] Hua, X. (2024, November). Design and Implementation of a Game QoE Monitoring and Evaluation System Driven by Network Traffic Analysis. In International Conference on Cognitive based Information Processing and Applications (pp. 149-161). Singapore: Springer Nature Singapore.

[16] Huang, J. (2025, August). Research on Multi-Model Fusion Machine Learning Demand Intelligent Forecasting System in Cloud Computing Environment. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-7). IEEE.

[17] Zhou, Y. (2024, November). Construction of a Multi-factor Quantitative Stock Selection System for the New Energy Industry Based on Microservices Architecture and Machine Learning Components. In International Conference on Cognitive based Information Processing and Applications (pp. 163-174). Singapore: Springer Nature Singapore.

[18] Sun, Q. (2026). Research on Lightweight Intelligent Dialogue Systems Based on Semantic Entity Enhanced Intention Recognition and Rule Retrieval Generation Hybrid Models.

[19] Wang, Y. (2026). Research on the Application of Artificial Intelligence in Supply Chain Risk Early Warning.

[20] Liu, H. (2026). Research on Dynamic Price Prediction of E-commerce Based on Time Series Modeling.

[21] Yu, X. (2026). Strategy Models and Practical Research of Growth Marketing under the Background of Digital Transformation.

[22] Yin, J. (2026). Research on Financial Time Series Prediction and Multiscale Correlation Based on the Fusion of Network Big Data and Deep Learning.

[23] Hou, Y. (2026). Research on Server Performance Stability Assurance Mechanisms during Cross-Generation Computing Platform Upgrades.

[24] Han, X. (2026). Research on Process Decision-Making Behavior under Incomplete Information Conditions in Automobile Manufacturing Systems.

[25] Yu, X. (2026). Exploration of Multi-Channel Conversion Path Optimization Methods Based on A/B Testing.

[26] Zheng, H. (2026). Research on Edge Computing Network Task Scheduling and Resource Management Optimization Based on Artificial Intelligence Technology.

[27] Zhixian Zhang. Research on Model Engineering Integration Methods for AI Systems Based on Data-Driven Intelligence. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 140-149.

[28] Ma, X. (2026). Research on End-To-End Reliability Modeling and Optimization of Service Grid.

[29] Zinuo Wang. Value Reassessment Logic of Resource-Based Enterprises in the Context of Energy Transition. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 141-149.

[30] Zelin Wang. Data Analysis and Risk in Supply Chain Management. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 132-140.