3.	Velocity	Could be defined as the speed of data traveling from one side to another or moves around and the speed of processing it with high rate of receiving data and information [1] .
4.	Veracity	We need clear and definite answer for a very important question, does data comes from a reliable source [1] .
5.	Validity	How quality consistence, preciseness, reasonableness and correctness the data for its intentional use [1] .
6.	Value	Unless turning the enormous amount of data in big data into value, it could be useless and unusable [1] .
7.	Variability	Variability in big data’s circumstances means variability in the data, which required to be found by deviation and aberration detection methods leading for any relevant analytics to occur [1] [4] .
8.	Venue	Big data is distinguished by its distributed heterogeneous data from various platforms, from numerous owners’ systems, with different formatting and access needs, private or popular [1] [5] .
9.	Vocabulary	All metadata shapes like data models, schema, semantics, ontologies, taxonomies, and other contents that describe the data’s structure, syntax, content, and origin [1] [5] .
10.	Vagueness	The meaning of found data is often very unclear, not only has how much data been available but also how much it is not obscure [1] [5] .
11.	Vulnerability	This means that no system is perfect, which means it’s probable there is a way for its hardware or software to be agreement, successively meaning that any associated data can be tacked or manipulated [1] [4] .
12.	Volatility	What time does remain data valid and should be stored. How old does data need to be before it is considered irrelevant [1] [4] .
13.	Visualization	Refers to the application of more recent visualization techniques to explain the relationships between data and can display real-time changes and more illustrative graphics, thus going beyond pie, bar and other charts [1] [4] .
14.	Viscosity	It is occasionally used to express the delay, latency or lost time in the data relative to the phenomenon being described [1] [6] .
15.	Virality	Measures the rate at which data can propagate through a network [1] [6] .
16.	Virtual	Enterprises and other groups can benefit from big data virtualization because it authorizes them to use all the data assets they gather to accomplish various goals and objectives [1] .
17.	Valences	It is a measure indicating how dense the data is [1] .
18.	Viability	Viability could be seen as carefully choosing those attributes in the data that are most likely to forecast outcomes that matter most to organizations [1] .
19.	Virility	With Big Data it means that it creates itself. The more Big Data you have, the more Big Data gets strength and forceful [1] [7] [8] [9] [10] .
20.	Vendible	The very existence of client’s for Big Data shows crucially that it is appreciable—this is evident from the communication of some known means of trading with subscribers data [1] [7] [11] [12] .
21.	Vanity	Vain of data means that it is glad with the effect it produces on other individuals, [1] [7] [11] [12] .
22.	Voracity	Big Data is potentially so insatiable that it may achieve the influence, manage and the possibility to consume itself [1] [7] [11] [12] .