Innovation Management Software contains complex data with many different variables. This data is usually presented in tabular form or with isolated graphs that visualize a single independent aspect of a dataset. However, displaying this data with interconnected, interactive charts provide much more flexibility and opportunities for working with and understanding the data. Charts that show multiple aspects of the data at once can help in uncovering hidden relationships between different aspects of the data and in finding new insights that might be difficult to see with the traditional way of displaying data. The size and complexity of the available data also invites analyzing it with machine learning techniques. In this thesis it is first explored how machine learning techniques can be used to gain additional insight from the data and then the results of this investigation are used together with the original data in order to build a prototypical dashboard for exploratory visual data analysis. This dashboard is then evaluated by means of ICE-T heuristics and the results and findings are discussed.