Core Viewpoint - The article discusses the evolution of collaboration between departments within the company, emphasizing the transition from isolated data handling to a shared data language and co-creation, ultimately leading to a more efficient and integrated approach to problem-solving and product development [4][5][10]. Group 1: Challenges of Departmental Silos - Departmental silos create barriers that hinder effective communication and collaboration, leading to conflicts in objectives and a lack of a unified approach to problem-solving [3]. - The division of responsibilities among departments, while enhancing specialization, results in a fragmented view of issues, making it difficult to establish a cross-departmental mechanism for addressing problems [3]. Group 2: Initial Collaboration and Data Sharing - The initial collaboration between the Ideal Lianshan team and the thermal management team began with addressing poor cloud signal data quality, leading to the development of a common analytical framework [4]. - The shift from a "data request-result" model to a shared data language allowed both teams to engage in meaningful dialogue using the same data and metrics [4][5]. Group 3: Evolution of Collaborative Methods - The collaboration evolved from merely sharing data to co-creating solutions, focusing on common goals and fostering trust through transparency [5][6]. - The implementation of automated testing processes helped alleviate the burdens faced by engineers during extreme conditions, showcasing the practical benefits of this collaborative approach [5]. Group 4: Productization of Collaboration - Over three years, the company expanded its collaborative model to include supply chain and production line processes, developing AI-driven solutions to intercept quality issues at the source [9]. - The establishment of a standardized, replicable methodology for data science projects has transformed the collaboration into a sustainable and scalable productized approach [10]. Group 5: Achievements and Future Aspirations - The company has accumulated significant achievements, including 83 data science projects, 3545 warning models, and extensive monitoring capabilities across production lines and suppliers [10]. - The goal is to promote this collaborative model further, enabling seamless cooperation among individuals, AI, and across departments to address real business challenges [11].