the VS of big data

 




Big data definitions may vary slightly, but it will always be described in terms of volume, velocity, and variety. These big data characteristics are often referred to as the “3 Vs of big data” and were first defined by Gartner in 2001.

Volume

As its name suggests, the most common characteristic associated with big data is its high volume. This describes the enormous amount of data that is available for collection and produced from a variety of sources and devices on a continuous basis.

Velocity

Big data velocity refers to the speed at which data is generated. Today, data is often produced in real time or near real time, and therefore, it must also be processed, accessed, and analysed at the same rate to have any meaningful impact. 

Variety

Data is heterogeneous, meaning it can come from many different sources and can be structured, unstructured, or semi-structured. More traditional structured data (such as data in spreadsheets or relational databases) is now supplemented by unstructured text, images, audio, video files, or semi-structured formats like sensor data that can’t be organized in a fixed data schema. 

In addition to these three original Vs, three others that are often mentioned in relation to harnessing the power of big data: veracity, variability, and value.  

  • Veracity: Big data can be messy, noisy, and error-prone, which makes it difficult to control the quality and accuracy of the data. Large datasets can be unwieldy and confusing, while smaller datasets could present an incomplete picture. The higher the veracity of the data, the more trustworthy it is.
  • Variability: The meaning of collected data is constantly changing, which can lead to inconsistency over time. These shifts include not only changes in context and interpretation but also data collection methods based on the information that companies want to capture and analyse.
  • Value: It’s essential to determine the business value of the data you collect. Big data must contain the right data and then be effectively analysed in order to yield insights that can help drive decision-making. 

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