archive.Joe Lamantia.com

Unsurprisingly, definitions of Big Data run the gamut from the turgid to the flip, making room to include the trite, the breathless, and the simply un-inspiring in the big circle around the campfire. Some of these definitions are useful in part, but none of them captures the essence of the matter. Most are mistakes in kind, trying to ground and capture Big Data as a ‘thing’ of some sort that is measurable in objective terms. Anytime you encounter a number, this is the school of thought.

Some approach Big Data as a state of being, most often a simple operational state of insufficiency of some kind; typically resources like analysts, compute power or storage for handling data effectively; occasionally something less quantifiable like clarity of purpose and criteria for management. Anytime you encounter phrasing that relies on the reader to interpret and define the particulars of the insufficiency, this is the school of thought.

I see Big Data as a self-defined (perhaps diagnosed is more accurate) condition, but one that is based on idiosyncratic interpretation of current and possible future situations in which understanding of, planning for, and activity around data are central.

Here’s my working definition: Big Data is the condition in which very high actual or expected difficulty in working successfully with data combines with very high anticipated but unknown value and benefit, leading to the a-priori assumption that currently available information management and analytical capabilties are broadly insufficient, making new and previously unknown capabilities seemingly necessary.

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