Visualizations designed to support multi-attribute decisions often use colors to encode the identity of the attributes. This approach facilitates mapping of attributes across multiple coordinated views but it has certain limitations: colors often communicate semantics (e.g., red stands for “danger”) deemed to influence the user’s preference, and qualitative color palettes are of limited scalability. We are currently developing a tool with an alternative approach, DCPAIRS: a pairs plot based decision making support tool that employs a compact overview of the decision space and uses visual encodings that communicate uncertainty and suboptimal preference elicitation. Instead of encoding the identity of attributes we use colors for user-authored annotations to support the decision making process. A use case scenario of a prospective undergraduate student choosing a university from the “QS world university ranking” dataset illustrates the functionality of the tool.