Earth observation data should help make decisions. However, all Earth observation data have an associated uncertainty arising from a number of error effects in the measurement process that vary in time and space, including limitations of the measurement and calibration process, uncertainties in auxiliary data, approximations in the data processing, and inhomogeneities in the observed field. Data producers thus strive to provide reliable uncertainty estimates alongside their products that should help inform decisions that are based on these products. However, data users often struggle to make sense of uncertainty information, because it is usually expressed as the statistical spread in the observations (for example, as random error standard deviation), which does not relate to an intended use of the data. That is, expressing data and their uncertainty as “x plus/minus y” does not answer the really important question: How much can I trust “x”, or any use of or decision based upon “x”? In this talk, we present a Bayesian framework that can be used to transform Earth observation product uncertainties into more meaningful, actionable information by explicitly quantifying the resulting probabilities of events of interest. We demonstrate the merit of this framework using two case examples: (i) monitoring drought severity based on soil moisture; and (ii) estimating coral bleaching risk based on sea surface temperature. We first show how looking at deterministic state estimates from Earth observations alone can be misleading, and that any decisions based on these estimates are unlikely to be the best course of action. We then show how typical data representations like “the state of this variable is “x plus/minus y” can be transformed into more informative statements such as “the data and their uncertainties suggest that we can be “z” % confident that…” and discuss how such an approach can help data users make better decisions, thus maximizing the socioeconomic merit of Earth observation data.