Many vendors and pundits have attempted to augment Gartner’s original “3Vs” from the late 1990s with clever(?) “V”s of their own. However, the 3Vs were intended to define the proportional dimensions and challenges specific to big data. Other “V”s like veracity, validity, value, viability, etc. are aspirational qualities of all data, not definitional qualities of big data. Conflating inherent aspects with important objectives leads to poor prioritization and planning. For example, if you’re like many organizations, your terabytes of streamed sensor, log file or multimedia data may not have veracity (data quality) issues at all, but your megabytes of master data may be in total disarray.
As author and analytics strategy consultant Seth Grimes observes in his InformationWeek piece Big Data: Avoid ‘Wanna V’ Confusion, “When a concept resonates, as big data has, vendors, pundits and gurus — the revisionists — spin it for their own ends….In my opinion, the wanna-V backers and the contrarians mistake interpretive, derived qualities for essential attributes.”
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