Unsupervised learning algorithms like self-organizing Kohonen maps are a promising approach to gain an overview among massive datasets. With UltraPINK, researchers can train, inspect, and explore self-organizing maps, whereby the toolbox of interaction possibilities grows continually. Key feature of UltraPINK is the consideration of versality in astronomical data. By keeping the operations as abstract as possible and using design patterns meant for abstract usage, we ensure that data is compatible with UltraPINK, regardless of its type, formatting, or origin. Future work on the application will keep extending the catalogue of exploration tools and the interfaces towards other established applications to process astronomical data. Ultimatively, we aim towards a solid infrastructure for data analysis in astronomy.