The supply chain resilience and data analytics capability has generated increased interest in academia and among practitioners. However, existing studies often treat these two streams of literature independently. Our study model reconciles two different streams of literature: data analytics capability as a means to improve information-processing capacity and supply chain resilience as a means to reduce a ripple effect in supply chain or quickly recover after disruptions in the supply chain. We have grounded our theoretical model in the organizational information processing theory (OIPT). The four research hypotheses are further tested using responses from 213 Indian manufacturing organizations collected via a survey-based pre-tested instrument. We further test our model using variance based structural equation modelling, popularly known as PLS-SEM. All of hypotheses were supported. The findings of our study offer a unique contribution to information systems (IS) and operations management (OM) literature. The findings further provide numerous directions to the supply chain managers. Finally, we note our study limitations and provide further research directions.