To manage the growing demands of this day and age, there is a need to increase the capacity and performance of tools and methods employed for analysis of data. Big Data requires new solutions to improve the capacity and to exploit the inherent value it brings with itself. Indeed, with the exponential growth of data, traditional data mining algorithms have been unable to meet important needs in terms of data processing, but it can be very costly and time taking for development of hardware which can handle such loads. Also, the hardware thus developed may become insufficient in a matter of months, given the rate at which the data is increasing and evolving. In order to exploit this voluminous data without much financial and time overhead, efficient processing model with a reasonable computational cost of this huge, complex, highly dynamic and heterogeneous data set is needed.

The above Figure 1 defines the methodology which was adapted to carry out the research work, Firstly the papers relevant to big data were collected, then papers related to big data operation management were filtered out followed by the brief discussion of big data in operation management and various approaches to big data operation management

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