When building decision-making models from disparate observations, there are no set rules to guide the designer on how to organize available information, how to classify vital aspects, how to emphasize important ones in the aggregation process, and how to deal with conflict and uncertainty in the aggregation procedures. This paper draws on the experience of structuring a business and risk model that evaluates service requests, which requires not only dynamic context-based decisions, but also situational and behavioral perspectives, with high uncertainty and wide variations of attribute styles. This study focuses on design issues that affect classification and aggregation options, such as corroboration, primacy and discord, and provides examples of classified key-factors that demonstrate the design issues. The paper suggests procedures and algorithms to fit the design, but shows that there is no universal method - there is no 'one-size-fits-all'