Had the pleasure of hosting Jim Gavigan on my latest podcast episode, where we deep-dived into "Data-Driven Optimization in Process Industries." We discussed leveraging data for efficiency, the challenges of data quality, and choosing between foundational principles and cutting-edge ML algorithms. Jim also highlighted the significance of tools and strategies in this sphere, emphasizing the urgency of digitizing domain knowledge in the face of an impending knowledge drain. Jim, is the President and Founder of Industrial Insight, Inc. where he helps industrial companies turn data into actionable information to deliver tangible results for their organization. Here is the outline of our conversation: ✅ Principles of Data-Driven Process Optimization✅ Opportunities in data-driven optimization and use case✅ Challenges faced by industries when implementing data-driven optimization strategies?✅ Overcoming the hurdles of data quality and fidelity?✅ First principles vs. Multivariate data analysis vs. ML algorithms?✅ Evaluating readiness to effectively integrate AI/ML in process optimization✅ Tech stack for data-driven optimization✅ Impending knowledge drain, and capturing domain knowledge into digital tools.