cool hit counter The question arises, how many V's does Big Data have to characterize?_Intefrankly

The question arises, how many V's does Big Data have to characterize?


When the characteristics of big data are mentioned, many people immediately think of 3Vs and 4Vs, so how many Vs does big data actually have?

In the evolving stage of Big Data, the industry is gradually improving its understanding of the characteristics of Big Data and covering a more comprehensive scope.

1、3V

In 2001, META Group analyst Lenny put forward the viewpoint of "3D data management" on big data in a report, that is, big data will develop in three directions: high speed, variety and volume, and proposed three characteristics: high speed (Velocity), variety (Variety) and scale (Volume), collectively known as 3V.

The 3V characteristic is the most representative characteristic of Big Data, recognized by McKinsey, IBM, Microsoft and many other companies and constantly mentioned in Big Data reports. IDC (International Data Corporation) defines Big Data by citing the 3Vs: "Big Data technology is a new generation of technologies and architectures designed to extract value from large volumes of multi-category data through very rapid (velocity) collection, discovery and analysis at affordable costs."

2、4V

The 4Vs are also widely recognized Big Data characteristics, adding the dimension of Value to the 3Vs, mainly emphasizing that Big Data has high overall value but low value density.

Volume, Variety, Velocity and Value, collectively known as the "4 Vs".

In addition, another view of big data is "4V+1O", which adds an O to the 4V, that is, data online, emphasizing that data is always online and can be called and calculated at any time, which is also one of the characteristics different from traditional data.

3、5V、6V、7V、8V

With the continuous development of big data technology and the increasing complexity of data, new assertions of big data characteristics have been continuously proposed, adding accuracy (Veracity) to the 4Vs, emphasizing that meaningful data must be true and accurate; adding dynamism (Vitality), emphasizing the dynamics of the entire data system; adding visualization (Visualization), emphasizing the manifestation of data; adding legitimacy (Validity), emphasizing the legitimacy of data collection and application, especially for the reasonable use of personal privacy data.

The following figure shows the characteristic graph from 3V to 8V


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