Decision tree analysis and Monte Carlo simulation are the most commonly used tools in decision and risk analysis. But other tools such as optimization, options analysis, and combinations of these various tools can also be useful. This article examines the importance of data analysis and the nature and application of these other tools. Regardless of the principal tool used in risk analysis--Monte Carlo simulation or decision trees--empirical data may play an important role. Similarly, the input distributions selected for a Monte Carlo model are easier to justify when analogous data is available to support the choices of distribution type and value of defining parameters, such as mean and standard deviation.