Symbolic AI reasoning utilizes symbols and logical rules to represent knowledge, which compactly represents the conditions for solutions. Research in many areas has primarily focused on qualitative reasoning, such as searching for a single solution or optimal solutions. Researchers also investigated enumeration and quantitative reasoning, such as counting or conditional probabilities. These techniques can also be used to understand better the solution space of given symbolic representations. For example, which element of the solution is very significant? Unfortunately, strong reasoning techniques (enumeration and counting) are computationally expensive and practically often entirely out of reach. Therefore, we consider a notion that lies between qualitative and quantitative reasoning, called facets, which enables the understanding of the significance of parts of a solution. We illustrate quantitative reasoning and reasoning with facets for navigating solution spaces in answer-set programming (ASP).  Moreover, we discuss its computational complexity and practical use.