The key objectives were as follows: Propose an end-to-end playbook for data science managers working with operations partners to implement predictive maintenance while navigating corporate systems and structures. Practice how to create and evaluate a predictive maintenance model from publicly available data sources. Discover what data and systems it will take to implement the approach cross-functionally, end to end. Create a data strategy that effectively communicates the value of pursuing predictive maintenance over preventive strategies, while helping the organization to gauge how it’s doing across the board to deliver service to its customers. Discuss translation of the concepts into digestible formats for operations leaders who need to support the idea for full adoption and implementation. Recommend areas of opportunity for development and future study as it relates to implementing predictive maintenance