Core Insights - The essence of system development is to solve problems and create value through technology, rather than merely writing code [1] - A valuable system integrates business goals, user needs, and technical capabilities, avoiding the pitfall of becoming an unused tool after development [1] Value Anchoring: Focusing on Real Needs - The first step in system development is to clarify the value the system aims to create, rather than discussing technical frameworks [6] - Demand research should penetrate superficial requests to identify core needs, ensuring that the system addresses actual pain points [6] - Not all demands are worth developing; prioritizing based on value and cost is essential to focus resources on solving real problems [6][7] Consensus on Requirements - After determining requirements, all stakeholders must align on the value and acceptance criteria of each function to avoid misunderstandings [7] Role Collaboration - System development is a collaborative effort involving business, technical, design, and testing roles, rather than a solo endeavor by the technical team [8] - Clear role definitions and proactive communication among teams are crucial to ensure that all aspects of the project align with business needs [8] - Iterative development with frequent communication helps to identify and resolve issues early, avoiding the pitfalls of traditional waterfall methods [8] Tool Collaboration - Utilizing a unified collaboration platform for managing requirements, task assignments, and progress tracking is essential to avoid confusion [9] Quality Control: From "Usable" to "User-Friendly" - Quality control should be an ongoing process that encompasses functionality, user experience, and security [12] - Functionality must match requirements precisely to avoid operational issues, with thorough self-testing during development [12] - User experience should be prioritized by incorporating user feedback into the development process to simplify operations [12] - Security measures must be integrated from the start, including data encryption and vulnerability assessments before launch [13] Continuous Evolution: Adapting with Business Growth - System deployment marks the beginning of ongoing value delivery, necessitating a feedback and optimization loop [16] - A data-driven iteration mechanism should be established to monitor core metrics and identify areas for improvement [16] - Systems must be adaptable to changes in business models, ensuring they can support new functionalities as needed [16] - User feedback should be actively collected and incorporated into future iterations to enhance the overall experience [16]

Core Viewpoint - The rapid development of internet and artificial intelligence technologies is driving society into a "data-driven and intelligent empowerment" era, posing new challenges for ideological and political education in higher education institutions [1] Group 1: Challenges and Adaptations in Ideological Education - Youth students, as frequent users of network technology, are experiencing changes in their learning and interaction modes due to the widespread application of "data + algorithms," necessitating an expansion of educational space, optimization of content supply, and innovation in value guidance [1] - The algorithmic recommendation mechanism has led to a clear trend of information dissemination becoming more segmented, which, while improving matching efficiency, raises concerns about the coverage and value guidance of ideological education resources in the digital space [2] Group 2: Enhancing Visibility and Engagement - Higher education institutions need to increase the visibility, weight, and priority of ideological education content within algorithmic recommendation mechanisms, ensuring that mainstream values guide algorithms and enhancing the attractiveness and persuasiveness of educational content through data analysis and content optimization [3] - Collaboration with mainstream online platforms is essential to embed value weights in recommendation models and establish stable content delivery mechanisms, while also enhancing the construction and management of campus media matrices to facilitate the production and dissemination of educational content [3] Group 3: Intelligent Transformation of Educational Models - The shift towards a data-driven and interactive approach in ideological education requires a focus on student-centered strategies, utilizing real-time analysis of fragmented online behaviors to predict students' ideological dynamics and needs [4] - A balance between personalized and collective education is crucial, with the development of algorithmic models that integrate mainstream value information to guide students' cognitive processes and enhance emotional resonance [5] Group 4: Collaborative Ecosystem for Ideological Education - The innovation logic of ideological education in the digital age involves not only technological upgrades but also dynamic updates of educational concepts, promoting a systematic and ecological layout that encourages multi-party participation and resource sharing [7] - The development of an ecological approach to ideological education necessitates leveraging the advantages of different digital platforms and integrating online and offline educational activities to enhance the practical appeal and effectiveness of the education [8]

Group 1: Core Concepts of Collaboration and Innovation - The ultimate pursuit of innovation is not merely a competition at the technical level, but rather achieving sustainable social value through collaborative co-creation [1] - Bayer's three business units demonstrate the practice of "collaborative co-creation" by empowering local innovation, extending value reach, and activating industry dynamics [1][4] - Bayer Co.Lab aims to accelerate local biotech innovation in China by linking global networks and empowering startups through an open ecosystem [3][7] Group 2: Bayer Co.Lab and Its Impact - Bayer Co.Lab has successfully integrated with local innovation, exemplified by the rapid growth of Ruizheng Gene, which achieved significant milestones after joining the platform [3][4] - The platform employs a venture capital-style rigorous selection mechanism to identify and nurture promising innovations that align with Bayer's global strategic focus [3][4] - Bayer Co.Lab has been recognized as an open innovation center, facilitating connections between policy resources and industry ecosystems [7] Group 3: Digital Health Services and Consumer Reach - Bayer's health consumer division leverages digital platforms like Meituan to transform laboratory innovations into accessible health services, creating a comprehensive service ecosystem [8][10] - The collaboration with Meituan has enabled Bayer to overcome traditional retail limitations, enhancing the efficiency of health service delivery [10][11] - Bayer's digital strategy has successfully expanded its customer base, reaching diverse demographics including young users and underserved markets [12] Group 4: Agricultural Initiatives and Local Adaptation - Bayer's "Lingfeng Plan" aims to empower small farmers in China by providing innovative products, technology, and services tailored to local agricultural conditions [13][19] - The plan addresses the unique characteristics of China's agricultural environment, focusing on localized service models to meet farmers' needs [15][19] - Through partnerships with retailers, Bayer has established a network of "Lingfeng Stores" to enhance service delivery and support for farmers [16][19] Group 5: Sustainable Value Creation - Bayer's initiatives reflect a commitment to balancing economic benefits with social value, achieving sustainable value gains through collaborative efforts [19] - The integration of global agricultural innovations with localized service networks is key to addressing the challenges faced by small farmers [19] - The overarching theme of collaborative co-creation enhances the ecological dynamics of Bayer's strategic layout and practical value delivery [19]