In the digital world, organizations across different sectors such as education, retail, finance, and healthcare are struggling to manage massive amounts of data. A critical challenge is handling overlapping data from multiple sources, which often leads to inconsistencies and reliability issues. While combining data from multiple sources can strengthen the credibility when the information matches, it creates significant problems when different sources provide conflicting information about the same attributes or events. Data fusion has emerged as a solution to these challenges. This approach combines data from different sources to create consistent and comprehensive information. The key aspects of data fusion include managing the database relationship, resolving conflicts, determining accurate information from conflicting sources, and tracking data origins (provenance). Although many papers review data fusion in specific fields, this paper takes a practical approach. Instead of presenting another literature survey, we focus on explaining the key steps in the data fusion process and examining the most effective techniques for implementing each step.