Synonymous with innovation, Research and Development (R&D) is all about competitive edge. Generative AI has emerged as an R&D catalyst, promising to unlock new use cases and unexplored opportunities to increase margins without extra spending. This includes filing patents and identifying market demands and how to capture them with existing technology.
But before we can hand over scientific data to an LLM, we need to streamline its acquisition, management and access while laying a foundation for continuous innovation. Scientific data is different from other kinds of data—and unlocking its value presents unique challenges. This data’s commonly unstructured nature requires flexibility and the ability to easily accommodate evolving data models that can be used to test new ideas quickly.
Join an expert panel of seasoned scientists and information research leaders from the Dow Company as they discuss how they built a semantic data hub with the Progress Data Platform to capture 100+ years of R&D knowledge, facilitate its discovery across the organization and make it future-ready for years to come.
You will learn how to:
- Approach the unique challenges and complexities of R&D data management
- Lay the foundational data and information management groundwork to accelerate new ideas, innovation and AI adoption
- Use semantic knowledge graphs to consolidate and structure lab test data and standardize its meaning for machine learning (ML) and future analysis
- Modernize your data infrastructure systems to flexibly enable lab research
- Leverage AI to speed up the discovery of knowledge and synthesis of new ideas that drive business value
To learn how to prepare your R&D data for the future and empower your research teams to leverage LLMs and data for faster information discovery, register now.