Our cities are being redefined daily based on social, political and environmental factors. This creates substantial challenges for those that attempt to develop resilience strategies for cities. Resilience planning requires a set of assumptions often based on data; however, the dynamic nature of our growing urban environments has impeded our ability to rely on these suppositions. To account for the unpredictable ebb and flow of changes in our cities we have become heavily dependent on data modeling and analytics. The ability to collect and store data from a variety of systems in a cloud infrastructure has enabled the potential for resilience planning to be based on historical scenarios and societal context – prioritizing risks and issues based on multiple factors. As our infrastructure becomes “smarter” with the ability to capture more data and make decisions through machine learning algorithms, resilience plans may become less in touch with the citizens for whom the resilience strategies exist. Thusly, an emergent risk to the inhabitants of cities is the imbalance of qualitative versus quantitative feedback that is leveraged to develop and improve a city's resilience strategy.