Big Data are turning into another innovation center both in science and in industry and spur innovation movement to information driven design and operational models. We are confronted with a deluge of information produced and caught in computerized structure as a consequence of the headway of sciences, building and innovations, and different social, efficient and human exercises. This big data wonder escorts in another time where human attempts and investigative interests will be helped by not just human capital, and physical and budgetary resources, additionally information resources. The staggering administration produced data turn out to be too huge and complex to be successfully handled by conventional methodologies. The most effective method to store, oversee, and make values from the administration situated big data turn into an imperative exploration issue. In this paper, we addresses a few variables influencing Big Data Quality at different levels, including, gathering, preparing, and capacity. Data quality is critical for all data investigation issues. Generally speaking a Philosophy of Big Data may accommodating in conceptualizing and acknowledging big data science as an administration rehearse, furthermore in transitioning to data-rich fates with human and data elements all the more beneficially coinciding in shared development and coordinated effort. We first examine the measurements in big data and big data analysis, and after that emphasis our consideration on the inconsistency problems in big data and the effect of instabilities in big data examination and how to manipulate inconsistencies.

Big Data Data quality inconsistency in big data manipulating inconsistencies