methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size . human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes . variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations . the identified variable value combinations can then be then used to predict future trends underlying the business intelligence data . in another embodiment , an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified — as a static snapshot or as a time - varying evolution . similarly , differences in observations between two groups can be decomposed into multiple contributing sub - groups for each of the groups and pairwise differences among sub - groups can be quantified and analyzed .