The 4 Vs of Big Data

The 4 Vs of Big Data

The so-called 4 Vs are the defining properties or dimensions of Big Data. In other words, these are the specific attributes that qualify a large set of data as a Big Data. Nevertheless, these 4 Vs are volume, velocity, variety, and veracity.

What Are the 4 Vs of Big Data: The Properties or Characteristics of Big Data

1. Volume

One of the main properties or characteristics that make a set of data big is volume. It refers to the amount of data generated from digital communication applications as well as to the amount of data available to an organization. It is important to highlight the fact that an organization does not necessarily have to own the data as long as it can access them.

2. Velocity

Variety is another one of the properties or characteristics of Big Data. By definition, it is the speed at which large amounts of data are generated from different sources. Note that this is not limited to the speed of incoming data but also to the speed at which the data flow and are aggregated.

3. Variety

Another one of the 4 Vs of Big Data is variety. It corresponds to the various formats of data such as structured, unstructured, and semi-structured. Note that data now includes not only the structured and quantifiable ones but also the unstructured and semi-structured ones such as images and videos, social media usage, and user behavior, among others.

4. Veracity

Veracity is a property or characteristic of Big Data that corresponds to its quality or trustworthiness. A large data set should be reliable so that the information produced from processing it can be used to make informed decisions. The data being processed should be meaningful to the problem being analyzed.

Other Vs of Big Data: Value, Volatility, and Validity

Below are the other or additional Vs of Big Data:

5. Value

The worth of the data and the corresponding information extracted from processing or mining them. Having large sets of data is one thing, but unless they can be turned into something valuable, then they are useless. Big Data should give an organization value.

6. Volatility

The volatility of Big Data refers to how long it is valid and how long it should be stored. Organizations need to determine at what point their data are no longer relevant and valuable. Furthermore, certain data privacy laws have mandates on how long data should be stored.

7. Validity

The validity of Big Data is determined by a combination of its veracity, value, and volatility. Hence, it combines different dimensions such as usefulness and timeliness. Validity essentially refers to how accurate and correct that data are for their intended use.

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