As a business information category, “Big Data” (BD) is typically defined as; “one or more extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.” However, while the definition is easily read, what does that really mean as a practical matter, and more importantly, how does an enterprise develop, employ and achieve BD as a recurrent value-add?
The effectiveness of Big Data doesn´t simply exist on the basis of multiple databases shoehorned into a common framework and hope that the consequent mass of data will render meaningful information at the end of the day. Granted, record density is a central element, but that alone is a long way from establishing a final product value.
Nevertheless, while Mr. Laney gave us a reasonable intellectual baseline, there was much more to consider, such as how the central BD value proposition would be applied in the real world. In this event, various legacy linguistic and developmental doctrines were originally utilized.
However, as the market began to innovate, forcing demands for faster data identification and manipulation, pushed along by an interest in global access to raw data delivered by previously unknown channels such as mobility, these approaches began to be seen as weak links in the overall production chain since they couldn´t stay ahead of future trends fostered by the premise of BD´s much faster multi-nodal data identification/exchange model.
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