Core Insights - The first "2025 Retail Data Foundation Innovation Conference" was held in Shanghai, co-hosted by OceanBase, IT Dongfanghui, and EFCIO, where the "Integrated Cloud Database White Paper" was released, marking the first systematic exposition of integrated cloud database value in the retail industry [1] - The white paper emphasizes that in the era of "second-level decision-making" and "centimeter-level insights," integrated cloud databases are transitioning from being a "cost center" to a "growth engine" [1] - Retail enterprises are encouraged to focus on six dimensions for technology selection: architecture design, cost-effectiveness balance, high availability, IT integration capability, security compliance, and AI-native capabilities to establish a solid foundation for the new retail integrated data base in the AI era [1] Industry Challenges - The retail industry is facing dual challenges of pulse-like traffic shocks and AI transformation, necessitating a stable, efficient data foundation that supports AI applications as a key to success [2] - Over 90% of surveyed enterprises believe that generative AI will enhance productivity, yet traditional architectures suffer from data delays, system fragmentation, and resource redundancy, hindering real-time decision-making and AI innovation applications [2] - The retail sector's next competitive round is fundamentally about the capability of the data foundation, which requires a new generation of technology base that meets "full-time response, full-domain coverage, full-end adaptation, and full-staff collaboration" [2]

Core Insights - The first "2025 Retail Data Foundation Innovation Conference" was held in Shanghai, where the "Integrated Cloud Database White Paper" was released, marking the first authoritative report on the value of integrated cloud databases in the retail industry [1] - The white paper emphasizes that integrated cloud databases are transitioning from a "cost center" to a "growth engine" in the era of "second-level decision-making" and "centimeter-level insights" [1] - Key business scenarios analyzed include consumer services, supply chain management, and omnichannel operations, with case studies from 12 leading companies such as Pop Mart and Li Ning [1] Industry Challenges and Opportunities - The retail industry is facing dual challenges from pulse-like traffic impacts and AI transformations, necessitating a stable, efficient data foundation that supports AI applications [3] - Over 90% of companies believe generative AI will enhance productivity, yet traditional architectures suffer from data delays, system fragmentation, and resource redundancy, hindering real-time decision-making and AI innovation [3] - The retail sector must address three core challenges: managing "pulse-like traffic surges," transforming data into a "real-time decision engine," and evolving AI from a "value-add" to a "core infrastructure" [3]