Design research methods frequently involve the collection of field data – such as sensor data or subjective experience - to understand human activities and practices. However, exploring multidimensional subjective and objective data is challenging for designers, as it requires switching between different data types and relating them to identify larger patterns and understand the context behind observations. Our study addresses this problem by presenting Dimension Hopper - an interactive data visualization tool that uses multiple coordinated views (MCV) to facilitate the identification of patterns in the data. We demonstrate how the selected views and the designed interactions between the data can facilitate data exploration by presenting examples of how it can be used to identify behavioral patterns in field data. We also discuss the challenges that emerged in our initial exploration of the tool, leading to factors that need to be considered when designing visualization tools for the exploration of multidimensional field data in design research.