In the era of digital transformation, managing complex data applications in the electricity sector demands innovative approaches to enhance accessibility, accuracy, and strategic utility. This paper introduces and explores the implementation of three pivotal business federated data models tailored for electricity customer enterprises. Firstly, the Marketing Enterprise Information Screening Model filters and de-duplicates non-residential electricity users, ensuring the accuracy and reliability of customer records for effective marketing strategies. Secondly, the Equity Penetration-Based Economic Composition Labeling Model classifies enterprise nature through sophisticated equity analysis, enabling customized service strategies and pricing models. Thirdly, the Multi-source Information-Based Intelligent Enterprise Archive Matching Model integrates diverse data sources using advanced algorithms, ensuring comprehensive customer data integration and accuracy. Grounded in data middle platform capability architecture and data governance theories, these models optimize data assets and foster a cohesive business-data ecosystem. Their implementation supports digital transformation goals by facilitating a unified grid view, real-time monitoring, and efficient material management. Furthermore, they contribute to a multi-dimensional benefit management system, enhancing financial transparency and operational efficiency. By prioritizing data integrity, accessibility, and strategic alignment, these models propel our organization towards greater agility and competitiveness in the evolving energy landscape. As we refine and expand these frameworks, we anticipate continuous improvements in decision-making and operational excellence, cementing our leadership in the electricity sector’s digital frontier.