Big Data Analytics (BDA) has attracted significant attention from both academicians and practitioners alike as it provides several ways to improve strategic, tactical and operational capabilities to eventually create a positive impact on the economic performance of organizations. In the present study, twelve significant barriers against BDA implementation are identified and assessed in the context of Indian manufacturing Supply Chains (SC). These barriers are modeled using an integrated two-stage approach, consisting of Interpretive Structural Modeling (ISM) in the first stage and Decision-Making Trial and Evaluation Laboratory (DEMATEL) in the second stage. The approach developed provides the interrelationships between the identified constructs and their intensities. Moreover, Fuzzy MICMAC technique is applied to analyze the high impact (i.e., high driving power) barriers. Results show that four constructs, namely lack of top management support, lack of financial support, lack of skills, and lack of techniques or procedures, are the most significant barriers. This study aids policy-makers in conceptualizing the mutual interaction of the barriers for developing policies and strategies to improve the penetration of BDA in manufacturing SC.