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Big Data Analytics (BDA) has been emerging as enabler to the organizations in various industries to their efforts ranging from innovations of products to decision making. Small and medium organizations usually replicate the large organizations' BDA efforts to succeed, but effective operationalization of BDA to those contexts largely remain unanswered in the literature. Integrating the previous literatures, the authors offer a Six Steps BDA research framework in aid to context specific data driven decision making. These steps include problem recognition, review of past findings, variables selection & model development, collecting data & testing the model, data analysis, and insights-based actions. The steps in this generic sequential framework are co-influencing, which at any stage can be reversed to the previous stage resulting in correct and strengthen the whole model. To ensure the best use of this linear model, organizations require to understand the systematic approach and execution strategies of BDA research. To avoid the inherent challenges of Big Data like; abundance, heterogeneity, incompleteness, inconsistency etc., practitioners and scholars should remain focused on acquisition, storage, and processing of data. Organizations need to calibrate their talent capabilities reflecting the evolving expectations of data-driven decision making for ongoing dynamic environmental circumstances.

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