The fusion and analysis of data from multiple sources can bring business value to enterprises and individuals by increasing decision-making efficiency, revealing potential business opportunities, improving the customer experience and reducing costs. This Catalyst plans to use AI, big data, and ‘cloud computing plus’ technologies to facilitate integration of data from multiple sources. It will use machine learning and deep learning algorithms to automate the fusion and analysis of multi-source data to improve processing efficiency and accuracy. At the same time, big data processing frameworks and algorithms will be employed to process large-scale data from multiple sources and provide efficient query and analysis capabilities. Moreover, the elastic computing and storage capabilities of cloud computing will ensure the solution is always available and scalable. The Catalyst also intends to establish data governance mechanisms to ensure the quality, security, and privacy of data and protect the interests of users. To evaluate the impact and value of multi-source data fusion, the project team will compare metrics, such as data integrity, accuracy, and consistency - before and after fusion. It will also assess the impact of the solution on business growth in the big data industry by tracking metrics, such as business size, market share, and revenue before and after implementation. This will help evaluate the innovation and business opportunities arising from the solution. Another objective is to reduce costs: the team will compare financial metrics, such as data processing costs, storage costs, and analytics costs, before and implementation. The Catalyst will also evaluate user satisfaction with the solution through user feedback and questionnaires.