Business Intelligence (BI) is a widely recognized term encompassing analytical applications and IT systems supporting them. Collaborative Business Intelligence (CBI) represents a refinement of traditional BI, emphasizing the cooperative execution of BI tasks. The tourism industry is experiencing rapid growth, leading to heightened competition among businesses. Collaborative Business Intelligence transcends organizational boundaries, creating a virtual space where individuals contribute insights to improve decision-making. In this study, our focus is on developing an application tailored for tourists, empowering them to make well-informed decisions about destinations and activities. Our approach involves aggregating, cleansing, and structuring tourism data to offer a user-friendly interface equipped with collaborative analysis tools. This necessitates gathering data from disparate sources and consolidating it into a unified format. CubeJS serves as a tool in creating an additional layer between the database and the main application, facilitating the generation of an OLAP cube and an API for generating necessary cube slices. MongoDB, chosen as the NOSQL database, is selected for its user-friendly interface, compatibility with CubeJS, and performance capabilities. To populate the database, we utilize data from Google Maps obtained through the corresponding API. Through our experimentation with tourism data, we introduce the CBI framework tailored for the tourism domain. This framework comprises three primary components: 1) Data Exploration; 2) Shared Analysis and Insights; 3) Real-time Decision-making. By integrating these three key components, our CBI framework aims to enhance decision-making processes, foster collaboration among stakeholders, and ultimately improve the overall effectiveness and efficiency of decision making.