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Due to the fast development of new technologies, the Business Intelligence market is changing rapidly, forcing vendors to adapt their offerings to the customers’ needs. As the amount of data available to companies has been substantially increasing in the past years, the need of suitable software tools that perform the right analyses became essential, even in the small and medium sized business' environment. The previous literature,focused on big firms and traditional implementation of Business Intelligence solutions, highlighted the importance of understanding the key factors in successful projects. In the past few years, a new delivery model for Business Intelligence software is taking place: the cloud computing. To date, key factors for adopting cloud Business Intelligence in small and medium sized enterprises (SMEs) have not been systematically investigated. Existing studies have rarely considered these arguments and we lack of a proven framework. This paper is aimed to fill this gap and the structure of the article is subordinated to this objective. Firstly, the thesis offers an overview of the subject and the terminology used in it with the purpose of facilitating the understanding of a rather complex argument. Therefore, it starts with a short historical overview of the Business Intelligence sector, it defines the term Business Intelligence, and it explains both the characteristics of the Business Intelligence systems (cloud vs on-premise) and the importance of having a business intelligence solution for SME. Subsequently, the theoretical framework of this study is defined, combining the prior theories and empirical data collected through the interviews with four Business Intelligence vendors and customers. Initially, the existing Critical Success Factors (CSFs) of IT and BI projects proposed by different authors in the literature are reviewed. Afterwards, the evaluation criteria for the cloud software are taken into consideration. By integrating insights drawn from these studies, as well as adding new factors coming from the interviews, a framework has been created and utilized as a basis for the further questionnaire development. The choice of pursuing both the quantitative and qualitative approaches is aimed at improving the study’s reliability. Empirical data are mainly primary data, collected during a survey and four interviews, supported by secondary data such as general companies' reports, market and trends analysis from trustworthy sources. Based on the findings, the author of this thesis has ranked the key aspects of a cloud BI adoption in SMEs. It is revealed the most important key adoption factors that SMEs evaluate when purchasing a cloud BI solution are the level of software functionalities, the ubiquitous access to data, the responsive answers to customer support requests, the ability to handle big amount of data and the implementation