Resiliency is a key word for a broad range of service delivery organizations. It is defined as the ability of an organization to rapidly adapt and effectively respond to the disruptions in its operations. A service delivery organization delivers a set of services which are essentially specified by their required set of resources. The organization sets up an infrastructural network of resources required for the service delivery and assigns to each service, its required set of resources. It also keeps sufficient residual capacity of the resources for the purpose of contingency planning. At the time of a disruptive incident, it reallocates the resources to the affected services from its residual capacity to keep the service running while the effects of the disruptions are reversed. Such actions of reallocating the resources to deal with disruptions to the original allocation are called recourse actions. We develop a framework that enables a data and analytics driven approach to achieve efficient recourse actions based resiliency. Our framework is based on abstractions of three important aspects of a service delivery organization, namely, the infrastructural network of resources, the set of services in terms of their requirements of resources, and the set of disruptive scenarios that an organization has to contend with. Our model also captures the different dependencies that exist within the infrastructure network. For instance, if the power supply is affected, our model allows us to infer all the other infrastructure resources which get affected as a consequence of the lack of power supply. There are no benchmark datasets to test the quality of resiliency analytics because of two reasons: nascency of research in this area and the classified nature of the organizational data required for such analytics. So, we have developed a simulation engine aimed at mimicking real-life organizations. We demonstrate how our framework can be used to proactively identify critical scenarios that could have adverse impact on the service delivery of an organization. We then show how such a knowledge can be used to make intelligent allocation of resources to the services so as to enable efficient recourse actions. These two analyses highlight that our framework can essentially serve as a decision support system for resiliency. © 2012 IEEE.