Smart cleaning technologies powered by data integration are revolutionizing building management by   optimizing cleaning processes for efficiency and sustainability. This research explores how internal and external data sources can be integrated to optimize smart cleaning processes in buildings, addressing the research question: "How can internal and external data sources be used to optimize the process of smart cleaning in buildings?" The study identifies relevant data types, examines their integration into cleaning processes, and evaluates their impact on operational efficiency, resource optimization, and occupant well-being. A qualitative approach was employed, utilizing semi-structured interviews with key stakeholders in building management and smart cleaning. The findings reveal that internal data, such as occupancy and environmental sensor data, is vital for real-time cleaning adjustments, while external data, including weather forecasts and public health information, improves the responsiveness of cleaning strategies. The integration of these data sources leads to more efficient cleaning operations, reduced resource wastage, and greater occupant satisfaction. The research concludes that data-driven smart cleaning processes offer significant advantages in maintaining building hygiene and operational efficiency. The study recommends further investment in advanced data integration platforms and training for cleaning staff to maximize the effectiveness of data-driven cleaning strategies.