Knowledge graphs provide a way for us to capture and relate information into a representation that can mimic expert knowledge. One type of expert knowledge that has proven to be particularly useful in industry is reasoning by analogy, or using a replacement problem and solution pair to think about the solution to a new problem. At opposite ends of the ‘representation spectrum’ are logic systems, which can easily become overly constrained and complex, and statistical approaches, which can be easily influenced by nonsensical data. In this talk, I describe challenges and successes I’ve seen using analogical reasoning and how a knowledge graph approach combined with statistics and data can overcome deficiencies in both methods. To illustrate the case, I’ll demonstrate how our financial decision support application leverages analogical reasoning in several places to answer challenging questions when evaluating organizations.