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Three Vs of Big Data

The most common dimensions or characteristics of big data management are volume, velocity,
and variety (the 3Vs), but as systems become more efficient and the need to process data faster
continues to increase, the original data management dimensions have expanded to include other
characteristics unique to big data. In fact, Mark van Rijmenam proposed four additional
dimensions in his August, 2013 blog post titled "Why The 3Vs Are Not Sufficient To Describe Big
2
Data," the additional dimensions are veracity, variability, visualization, and value. Figure 3
illustrates the “Expanded set of Vs of Big Data.”

Figure 3: Expanded Vs of Big Data

3 Vs of Big Data Additional Vs

Volume: The amount of data being Veracity: Data must be able to be
created is vast compared to verified based on both accuracy and
traditional data sources. context.
Variability: Big data is extremely
Variety: Data comes from all types variable and always changing.
of formats. This can include data
generated within an organization as
well as data created from external Visualization: Analytic results from
sources, including publicly available big data are often hard to interpret;
data. therefore , translating vast amount
of data into readily presentable
graphics and charts that are easy to
Velocity: Data is being generated understand is critical to end-user
extremely quickly and continuously. satisfaction and may highlight
additional insights.

Value: Organizations, societies, and
consumers can all benefit from big
data. Value is generated when new
insights are translated into actions
that create positive outcomes.
Source: The IIA

2. https://datafloq.com/read/3vs-sufficient-describe-big-data/166.

8 — theiia.org