Big data-driven chemical product development
In this webinar, Jun Liu, Senior Marketing Manager at PerkinElmer Informatics, will explain the significance of incorporating big data in chemical product development to streamline the process and achieve better outcomes. Jun will delve into the various challenges associated with collecting, analyzing, and integrating data from different sources, such as data silos, data quality issues, and data security concerns. Several strategies to overcome these challenges, including creating a structured data lake, improving data quality, and leveraging existing data to form new insights will be presented.
The webinar will also explore the potential of machine learning and artificial intelligence in streamlining and optimizing the chemical product development process. By leveraging big data and advanced R&D data management software tools, chemical companies can streamline and accelerate their product development process, reduce costs, and stay competitive in the market.
Key learning objectives
- Learn how to use advanced R&D data management software to organize experimental data
- Understand how modern electronic lab notebook (ELN) tools can help streamline R&D workflows
- Discover how advanced data analytical tools can lead to better product insights
Who should attend?
- R&D team members from chemical companies such as coating, polymer, additive, material science, and food ingredient industries.
Certificate of attendance
All webinar participants can request a certificate of attendance, including a learning outcomes summary, for continuing education purposes.
Speakers
