[Wrap Up] Snowflake DATA FOR BREAKFAST Event Report
Snowflake held its 'DATA FOR BREAKFAST' event on March 17, 2026, showcasing the latest updates in AI and data utilization. The event featured keynotes, product demonstrations, and real-world success stories from enterprises like Nissin Foods and Mitsubishi Electric, highlighting the importance of integrated data foundations in the age of AI agents.
📋 Article Processing Timeline
- 📰 Published: March 28, 2026 at 00:29
- 🔍 Collected: March 28, 2026 at 21:59 (21h 29m after Published)
- 🤖 AI Analyzed: April 15, 2026 at 02:29 (412h 30m after Collected)
Snowflake Inc. (Headquarters: Chuo-ku, Tokyo; President: Tatsumichi Ukita), the provider of the AI Data Cloud, held its 'DATA FOR BREAKFAST' event on March 17, 2026, at Bellesalle Tokyo Nihonbashi, delivering the latest trends in data and AI. The event shared insights into the future of generative AI and data utilization, while introducing Snowflake’s latest updates and advanced customer use cases.
In the keynote session, Snowflake President Tatsumichi Ukita took the stage first. Ukita touched on the current status of generative AI, which has evolved from the 'search' phase to the 'consultation' phase, with rapid progress in corporate adoption of Agent AI. While pointing out challenges in security and governance as AI is deployed across entire organizations, he emphasized the crucial message: 'Without a data strategy, there is no AI strategy.' He remarked, 'Appropriate data preparation and storage are indispensable to maximizing the value of AI,' and introduced practical examples of how Snowflake utilizes its conversational data agent, 'Snowflake Intelligence,' across the company to realize operational efficiency.
Following him, Christian Kleinerman, EVP of Product, took the stage. Kleinerman noted, 'For companies to derive true value from AI, it is essential not only to use general-purpose AI models but also to link them with company-specific context and appropriate data.' After emphasizing the importance of an integrated data foundation that eliminates data silos and provides reliability and governance, he introduced Snowflake Intelligence and 'Cortex Code,' an AI coding agent that dramatically streamlines data management for developers using natural language. Furthermore, he touched upon the general availability of the transactional database 'Snowflake Postgres' and the acquisition of Observe to enhance observability, appealing to the platform's evolution as one that comprehensively supports companies from AI adoption to application construction.
In tandem with Christian's presentation, Sho Tanaka, Developer Relations Lead Developer Advocate at Snowflake, conducted demonstrations that showcased real-world examples, deepening the attendees' understanding. In the Snowflake Intelligence demo, he demonstrated the process from extracting sales data and creating graphs using only natural language instructions to obtaining concrete analytical insights for market expansion. Subsequently, he introduced methods for immediate integration with external data on AWS and the use of Cortex Code to automatically generate SQL code from natural language. In particular, he demonstrated an automatic personal information masking feature using AI functions, presenting a practical process for easily building AI agents in a fully managed environment while maintaining robust security and governance.
[Case Study Session: Nissin Food Holdings Co., Ltd.]
Nissin Foods Group's challenge: 'Snowflake Data x AI Agent Utilization' and 'Data Integration Strategy'
Kazuki Ogo, General Manager of the Data Science Office at Nissin Food Holdings Co., Ltd., took the stage to introduce the construction of a company-wide integrated database with Snowflake as the core product, along with practical AI agent use cases. The company has built a mechanism to extract necessary knowledge using AI from unstructured data, such as scattered blending information, to support data analysis for sales negotiation preparation in the sales department and complex product development such as 'Kanzen Meshi' (Complete Meal) in the R&D department. Ogo emphasized, 'To turn the value of AI into continuous corporate competitiveness, the assetization of unstructured data and the verbalization of tacit knowledge and business expertise from the field are indispensable.' He shared the company's advanced initiatives to aim for a data-driven enterprise by promoting data strategy and AI strategy as two wheels of the same cart.
[Joint Panel Session]
Frontline of Business Transformation through AI and Data Utilization, as Told by Mitsubishi Electric, AWS, and Snowflake
Mitsubishi Electric Corporation introduced the construction of its company-wide data utilization platform 'Serendie,' with Snowflake and AWS at its core, as well as practical AI use cases. The company has built a monitoring service that constantly monitors the operating status of FA equipment (electrical discharge machines) with AI to reduce downtime, and a mechanism in the design department to quickly and accurately extract necessary knowledge from massive amounts of past design documents using AI (RAG system). Takanao Mizuguchi, General Manager of the Platform Design & Development Department at the DX Innovation Center, spoke about future developments, stating, 'I want to create a platform that organizes data catalogs and metadata, connects them with each agent, and uses them cross-functionally within the company while building orchestration,' sharing the goal of an advanced AI utilization platform that links multiple AI agents.
In the keynote session, Snowflake President Tatsumichi Ukita took the stage first. Ukita touched on the current status of generative AI, which has evolved from the 'search' phase to the 'consultation' phase, with rapid progress in corporate adoption of Agent AI. While pointing out challenges in security and governance as AI is deployed across entire organizations, he emphasized the crucial message: 'Without a data strategy, there is no AI strategy.' He remarked, 'Appropriate data preparation and storage are indispensable to maximizing the value of AI,' and introduced practical examples of how Snowflake utilizes its conversational data agent, 'Snowflake Intelligence,' across the company to realize operational efficiency.
Following him, Christian Kleinerman, EVP of Product, took the stage. Kleinerman noted, 'For companies to derive true value from AI, it is essential not only to use general-purpose AI models but also to link them with company-specific context and appropriate data.' After emphasizing the importance of an integrated data foundation that eliminates data silos and provides reliability and governance, he introduced Snowflake Intelligence and 'Cortex Code,' an AI coding agent that dramatically streamlines data management for developers using natural language. Furthermore, he touched upon the general availability of the transactional database 'Snowflake Postgres' and the acquisition of Observe to enhance observability, appealing to the platform's evolution as one that comprehensively supports companies from AI adoption to application construction.
In tandem with Christian's presentation, Sho Tanaka, Developer Relations Lead Developer Advocate at Snowflake, conducted demonstrations that showcased real-world examples, deepening the attendees' understanding. In the Snowflake Intelligence demo, he demonstrated the process from extracting sales data and creating graphs using only natural language instructions to obtaining concrete analytical insights for market expansion. Subsequently, he introduced methods for immediate integration with external data on AWS and the use of Cortex Code to automatically generate SQL code from natural language. In particular, he demonstrated an automatic personal information masking feature using AI functions, presenting a practical process for easily building AI agents in a fully managed environment while maintaining robust security and governance.
[Case Study Session: Nissin Food Holdings Co., Ltd.]
Nissin Foods Group's challenge: 'Snowflake Data x AI Agent Utilization' and 'Data Integration Strategy'
Kazuki Ogo, General Manager of the Data Science Office at Nissin Food Holdings Co., Ltd., took the stage to introduce the construction of a company-wide integrated database with Snowflake as the core product, along with practical AI agent use cases. The company has built a mechanism to extract necessary knowledge using AI from unstructured data, such as scattered blending information, to support data analysis for sales negotiation preparation in the sales department and complex product development such as 'Kanzen Meshi' (Complete Meal) in the R&D department. Ogo emphasized, 'To turn the value of AI into continuous corporate competitiveness, the assetization of unstructured data and the verbalization of tacit knowledge and business expertise from the field are indispensable.' He shared the company's advanced initiatives to aim for a data-driven enterprise by promoting data strategy and AI strategy as two wheels of the same cart.
[Joint Panel Session]
Frontline of Business Transformation through AI and Data Utilization, as Told by Mitsubishi Electric, AWS, and Snowflake
Mitsubishi Electric Corporation introduced the construction of its company-wide data utilization platform 'Serendie,' with Snowflake and AWS at its core, as well as practical AI use cases. The company has built a monitoring service that constantly monitors the operating status of FA equipment (electrical discharge machines) with AI to reduce downtime, and a mechanism in the design department to quickly and accurately extract necessary knowledge from massive amounts of past design documents using AI (RAG system). Takanao Mizuguchi, General Manager of the Platform Design & Development Department at the DX Innovation Center, spoke about future developments, stating, 'I want to create a platform that organizes data catalogs and metadata, connects them with each agent, and uses them cross-functionally within the company while building orchestration,' sharing the goal of an advanced AI utilization platform that links multiple AI agents.