For many industry labs, scientific data has historically been generated to answer specific, immediate research questions and then archived to protect IP with little attention paid to the future value of reusing the data to answer similar questions. The resulting data often lack a consistent structure and quality. In addition, the highly manual workflows that produce this experimental data are complex, slow, and expensive. Altogether, this leads to most R&D labs today having surprisingly small datasets that are actually suitable for machine learning and AI.
Faced with this ‘small data’ situation, researchers and managers often feel that they may not yet benefit from pursuing data-driven approaches like Materials Informatics to new product development. They are not sure what can be done now to most effectively move forward. At Enthought, we have tackled many materials and chemicals product development challenges and have employed multiple techniques for getting the most value out of small data to meet innovation goals. In this webinar, we’ll present proven strategies and tips for how teams can make the most of what data they have and set a course towards continuous improvement through Materials Informatics.
Key Learning Objectives:
- How to develop a successful strategy for implementing digital R&D approaches across your organization in a conservative business environment.
- How to start benefiting from data-driven product development methods even without a lot of data.
- The critical role that chemical experts play in being successful with data-driven approaches.
- Several tips and tricks for building better ML models and AI recommendation systems with less data.
Who Should Attend:
- R&D Managers
- R&D Directors
- Senior R&D Researchers