DATAWiSE will develop and test building and building portfolio management tools, leveraging cross-sectoral lifecycle data on the basis of an open, secure, interoperable, and scalable framework. Utilizing cutting-edge artificial intelligence and advanced analytics, the methodology will integrate data from a variety of sources to provide a holistic understanding of building operations. DATAWiSE will design and implement a scalable data architecture that ensures data quality, privacy, interoperability, and sharing, and a Data Sharing Platform that will not only ensure interoperability and scalability but will also prioritize data sovereignty to secure data handling and preserve ownership control. DATAWiSE will develop: i) a Data-driven Building Performance Management (DBPM) toolkit which will harness Building Information Modeling (BIM) data in conjunction with advanced data mining techniques through a digital twin. ii) An AI-enhanced Lifecycle Data-driven Decision Support (LD2S) toolkit. This will serve as a versatile solution for well-informed decision-making across planning, renovation, and sustainability domains. Through these tools, the project will offer a suite of added-value services aimed at optimizing building management in various aspects: Electrical and Thermal Flexibility Management; AI-Powered Energy Forecasting and Optimization; Smart Sustainability & Comfort Balancing for Building Occupants; Adaptive Building Risk & Resilience Assessment; Circular Lifecycle Assessment; Predictive Maintenance; Integrated Sustainability Performance Management; Smart Readiness Assessment. Besides technological innovation, the project also incorporates a supportive market and policy framework designed to offer evidence-based pathways for widespread adoption and commercialization.