This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.