The Case for Seamless and Flexible Simulation Data Governance

Engineering and scientific teams are leveraging real and synthetic data, machine learning, and AI, alongside physics-based simulations, to accelerate innovation, but the rapid pace of product development necessitates a focus on regulatory compliance, quality, and user safety. To balance speed with these critical factors, companies must implement robust simulation data governance to ensure reliability, reproducibility, and traceability, which in turn enhances the entire product development process beyond just concept innovation. Effective governance must not hinder R&D velocity, but rather integrate seamlessly with engineering and scientific workflows, offering flexible data management solutions that cater to the unique complexities of simulation data, which spans diverse formats and volumes. The challenges posed by fragmented and disconnected data silos, which can delay decision-making and increase risk, highlight the need for integrated data management approaches that ensure data security and compliance with industry standards. Inconsistent simulation results, lack of compliance, and potential product quality issues underscore the business risks of ungoverned simulation data, necessitating structured governance to maintain data quality and support engineering productivity. Simulation governance involves establishing processes and standards that enhance the reliability and credibility of simulation outcomes, thereby supporting informed decision-making, minimizing compliance risks, and boosting innovation.