Our team aims to deliver an end-to-end, data-driven user experience. We help Samsung device users enjoy seamless and personalized services powered by integrated data platforms within the Samsung device ecosystem. We leverage knowledge graph technologies to accelerate and optimize the data integration process, enabling a comprehensive AI-driven user experience. The Personal Data Engine was launched in Galaxy S25 models in collaboration with the Mobile eXperience (MX) division. Moving forward, we're expanding this data platform to accelerate the development of personalized multi-device experiences.

A knowledge graph can be used to improve enterprise productivity in various use cases, including SCM/ERP, CRM, PLM, and document Q&A. Enterprise knowledge graphs are built on carefully defined taxonomies and ontologies that systematically define and disambiguate concepts, capturing relationships and hierarchies within the data. By classifying enterprise data according to these structures, the RAG system gains a deeper understanding of the context. This Graph RAG system enables it to effectively navigate the graph of interconnected data points and guide the data retrieval process by leveraging the semantic relationships encoded in the ontology. We are researching technologies to convert enterprise structured and unstructured data into knowledge graphs automatically and developing a Graph RAG system for retrieving relevant data from user natural queries. Additionally, we are developing versatile tools to help create and maintain enterprise knowledge graphs.

We analyze data to uncover hidden user needs and support robust decision making. Our framework gathers and processes data from a wide range of sources, including Samsung devices, consumer behaviors, and even supply chains. By coordinating with multiple teams and divisions, we define objective yet effective problem statements that lead to well-founded solutions. Through these domain and data centered approaches, we ensure a consistent and integrated user experience across the entire Samsung device ecosystem. We actively employ a variety of methodologies, including conventional algorithms, machine learning, statistical analysis, and natural language processing techniques, to enhance our analysis solutions. Beyond analysis, we offer a dynamic platform that enables evidence based decision making, streamlines workflows, and deliver deeper insights into user behavior.

To extract meaningful health insights from Samsung devices, our research encompasses a broad spectrum of technologies ranging from innovative health sensors to new digital biomarkers. Leveraging the health sensing solutions, we collect and analyze vast amount of health data for continuous management of chronic diseases and holistic monitoring of mental and physical well-being. Genomics, personal health records (PHR) and life logs serves as integral components of our digital biomarkers, enabling us to deliver highly personalized health care service.

The Data Cloud team focuses on hardware (HW) infrastructure and software (SW) solutions for large-scale, complex data management, including high-quality data acquisition, massive data storage / repository management, and business analytics tools. We custom design the system for our researchers’ needs and secure the cost-effective infrastructure needed to acquire and manage the large-scale datasets utilized by our scientists. Our data cloud technologies provide a competitive advantage to our organization by providing our researchers with a fast and modern environment to extract value from Samsung’s vast data resources.

Our AI infrastructure research and deployment focuses on developing a world-class AI development environment with integrated physical HW and advanced SW systems that provide complete end-to-end ML operations ranging from data processing to AI model deployment. We are developing and deploying autonomous and scalable deep learning technologies based on the latest cloud technology. Ultimately, we seek to provide our AI platform and infrastructure to all Samsung AI developers and build an ecosystem enabling them to collaborate and share to leverage each other’s experiences to grow and be more productive.