Data in biomedicine are characterised by their complexity, volatility and heterogeneity. It is these characteristics, rather than size of the data, that make managing these data an issue for their analysis. Any significant data analysis task requires gathering data from many places, organising the relationships between the data's entities and overcoming the issues of recognising the nature of each entity such that this organisation can take place. It is the inter-relationship of these data and the semantic confusion inherent in the data that make the data complex. On top of this we have volatility in the domain's data, knowledge and experimental techniques that make the processing of data from the domain a distinct challenge, even before those data are organised. In this article we describe these challenges with respect to a project that is using data mining techniques to analyse data from the kidney and urinary pathway (KUP) domain. We are using Semantic Web technologies to manage the complexity and change in our data and we report on our experiences in this project.