The purpose of this thesis is to discover what are characteristics of innovative economies and what are the factors that make some countries more innovative, through examining the data from Global Innovation Index, one of the most popular rankings that shows the innovative performance of different economies. This research was achieved by explaining the primary theories that relate to innovation, both as a concept and from a systemic point of view, introducing National Systems of Innovation. Additionally, approaches to innovation measurement and competitiveness will be introduced that have been useful in the analysis of differences between countries, and defining limitations of this thesis and used data. The quantitative analysis has been performed through unsupervised machine learning, particularly hierarchical clustering. Both discussion and conclusion focus on limitations related to analyzing aggregate indicators and criticism towards measures of innovativeness used in Global Innovation Index, as it is not possible to univocally state what makes some countries more innovative because the main framework is focused on many phenomena that are not directly related to innovation. However, the ranking can be used to gain many interesting insights on a macroeconomic scale, benchmark different countries based on a number of criteria, and identify advantages or obstacles to innovation.