Analyzing massive amounts of data and extracting value has become key across different disciplines. A plethora of approaches has been developed to analyze the deluge of data. Using these approaches, however, is not straightforward and many require a priori knowledge of the dataset to set parameters, such as the number of clusters, making their use challenging. Lack of knowledge about the dataset means either that the clustering algorithm has to be run multiple times with different parameters or expensive human intervention and in-depth analysis is required, significantly delaying the analysis and reducing its reproducibility. In this paper, we introduce the idea of simple assumptions about the global distribution of some property of the data leading to local, actionable insights. More specifically, we derive configuration parameters for a clustering method from global distribution properties of a dataset.