There is a lot of different definition for big data, such as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze”. Or “Big data is data too big to be handled and analyzed by traditional database protocols such as SQL”.

Both groups of authors previously mentioned go beyond the only size aspects of data when defining Big Data! Edd Dumbill explicitly conveys the multi-dimensionality of Big Data when adding that “the data is too big, moves too fast, or doesn’t fit the strictures of your database architectures”. This quotation allows us to see that extra characteristics should be added to large datasets to be considered as Big Data, or big size data as often found  throughout the literature.

Now it is assumed that size is not the only feature of Big Data. Many authors explicitly use the Three V’s (Volume, Variety and Velocity) to characterize Big Data. If the three V’s are largely found in the literature, many authors and institutes like IEEE focus on Big Data Value, Veracity and Visualization. This last “V” to notice how important it is to provide good tools to figure out data and analysis’ results.