What does the attribute “Veracity” imply in the context of Big data?

Veracity is one of the five V’s of Big data. In addition to the 3 V’s of Big data (i.e., Volume, Velocity, and Variety) there are other two V’s such as Veracity and Value which are collectively known as the 5 V’s of Big data. Veracity in Big data refers to the assurance of quality and accuracy of the data. Since the data is collected from multiple sources, there is a need to check the data for accuracy before using it for business insights.

Gathered data for Big data analysis could have missing data, may be inaccurate, or may not be able to provide real, valuable insight. It also can have inconsistencies and uncertainty, also the data available can sometimes get messy and quality and accuracy are difficult to control. So we can define Veracity in Big data as the level of purity in the collected data.

Big Data can sometimes become messy and difficult to use because of the multitude of data dimensions resulting from multiple disparate data types and sources. Data in bulk amounts could create confusion whereas fewer amounts of data could convey half or incomplete information.

For example, in the medical field, if data about a patient is incomplete, then proper medication cannot be given to the patient. Hence, both value and veracity together help define the quality and insights from the gathered data.

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Author: Ayush Purawr