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Other Big Data V's: Viscosity and Virality; Veracity and Value

Advances in social networks, mobile technologies, cloud computing and unified communications bring two additional characteristics that need to be dealt with in order to gather insight from the various data sources at our disposal: Viscosity and Virality. Viscosity characterizes the resistance to navigate in the dataset related for instance to variety of data sources, data flow rates or complexity of data processing required. Virality is a measure of the spread rate of data across the network. Time is an important characteristic along with rate of data proliferation.

Two other V's connected with Big Data are Veracity and Value. Veracity describes the quality and lineage of the data, to characterize data in doubt, conflicting data or noisy data and ultimately, data of which users are unsure how to deal with. Value is to characterize what value could come out of which data and how Big Data will enable the user to get better results from the data stored.

 

Other Big Data characteristics

Beside the seven V's defined above, there are other important characteristics to take into account in a Big Data case: the level of aggregation of the data stored as discarding original raw data may undermine the validity of Big Data analysis performed; availability and use of metadata (time, place, source, context, etc.) which help to significantly improve the effectiveness and usefulness of Big Data methods; and data signal-to-noise ratio as faint signals require fast and accurate methods in order to isolate the correct data effects and timely get to the right conclusions