Identification of quality parameters associated with 3V's of Big Data

Big Data approach uses an empirical process that does not lie on the understanding of underlying mechanisms, but lies on the observation of facts. Achieving high quality in Big Data is a critical issue for both the database researchers and practitioners. More explicit consideration must be given to data quality since data increasingly outlives the application for which it was initially designed. In this paper, identification of quality parameters is done which are compatible to the 3V's of big data which will further provide enhancement in achieving quality data to be stored in the repository. Good utilization of Big Data strengthens the performance and competitiveness of the firms by enabling better and faster results to its customer needs. In this paper GQM (Goal Question Metric) methodology is proposed to measure quality using metrics. It describes how to include data quality metrics to project, progress and maintain levels of quality in an organization. It helps to make a decision whether or not our current data satisfies our quality prospects.