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Yesterday we discussed the four parameters — the four V’s — that Forrester uses to describe and understand Big Data:
While the four V’s are catchy, like ‘Big’, the exact meaning of these descriptors can be fuzzy and not totally accurate. Velocity, for example, conjures up an image of something very fast moving. While, velocity is a relevant parameter that describes how data can be collected in real time and changes quickly as time passes, velocity isn’t a prerequisite for Big Data.
Economic and market data are two other good examples of high-velocity data sets. Another example of high velocity quickly changing data sets is the real-time mining of social media data for marketing and consumer sentiment studies.
But not all data sets are about change. Big Data doesn’t need to be about time-based volatility (another V) and change. Larger data sets can be used in solving problems by providing so much evidence that solutions can become crystal clear. Consider the data collected for medical trials or genomics; rather than track an ever-changing phenomena, in these cases, collecting more data helps bring into focus a trend or a solution.
Rather than ‘Big’, a better descriptor for ‘Big Data’ technology might be ‘Extreme’.
The McKinsey Global Institute says that a further ‘V’ to consider is the value in terms of productivity and efficiency that Big Data can bring to businesses.