We propose a new robust actionable prescriptive analytics framework that leverages past data and side information to minimize a risk-based objective function under distributional ambiguity. Our framework aims to find a policy that directly transforms the side information into implementable decisions. Specifically, we focus on developing actionable response policies that offer the benefits of interpretability and implementability. To address the potential issue of overfitting to empirical data, we adopt a data-driven robust satisficing approach that effectively handles uncertainty. We tackle the computational challenge for linear optimization models with recourse by developing a new tractable safe approximation for robust constraints, accommodating bilinear uncertainty and general norm-based uncertainty sets. Additionally, we introduce a biaffine recourse adaptation to enhance the quality of the approximation. Furthermore, we present a localized robust satisficing model that efficiently solves combinatorial optimization problems with tree-based static policies. Finally, we demonstrate the practical application of our framework through a simulation case study on risk-minimizing portfolio optimization using past returns as side information. We also provide a simulation case study on how the framework can be applied to obtain an interpretable policy for allocating taxis to different demand regions in response to weather information.