This handbook provides a practical roadmap for facility managers, particularly those without technical expertise, to leverage data-driven solutions in achieving asset management goals through smart buildings. The roadmap describes the optimalisation of existing BMS systems, as well as the steps towards integrating data from various sources to be able to develop advanced algorithmics.

Key Objectives:
1. Raise Awareness: Highlight current industry challenges and demonstrate how both high-tech and low-tech solutions can be employed to overcome these challenges.
2. Tool for Asset Management: Emphasize that smart buildings are not an end goal but a means to achieve predefined asset management objectives. Various tools are proposed to execute the discussed steps in the flow towards data integration.
3. Stakeholder Guidance: Offer a step-by-step guide for various stakeholders involved in the smart building value chain, from asset owners to service providers, to collaboratively achieve these goals.

Highlights:
• Strategic Roadmap: Provide a clear roadmap outlining the steps from setting asset management goals to implementing data-driven solutions and improving existing systems.
• Real-World Examples: Include specific examples and case studies to showcase the successful application of smart building technologies in real scenarios.
• Societal Impact: Address how smart buildings can contribute to reducing energy consumption and CO2 emissions, aligning with broader sustainability goals.
• Data Integration: Illustrate the importance of integrating various data sources (e.g., BMS, LMS) to optimize building operations and energy efficiency.

By following this handbook, facility managers can better understand how to utilize data and smart building technologies to en hance operational efficiency, achieve sustainability targets, and improve overall asset management. By following the roadmap, the readers end up with an integrated database which is needed to start developing advanced building specific algorithms like FDD and MPC.

The integration of data from a variety of systems is critical for developing smart building applications. Design, construction, maintenance, energy management, and automation data are among few. For this integration, having a comprehensive metadata schema is crucial. Semantic web technologies and domain ontologies have been proposed as a means of modeling and linking domains and their relationships. This study proposes a semiautomatic metadata schema generator that combines an ontology database and a text search engine to generate a metadata schema. The proposed method is tested for a real-world building, and the resulting metadata schema is used to link timeseries data to the sensors and equipment in the building automation system.