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My Own Definition of Big Data
Big Data refers to groups of data that reach the limits of processing capabilities of commodity data manipulation/analysis tools. Therefore, robust solutions have to be addressed to understand, relate and profit from the information found based on the processed data. The boundaries of what amount of data become processable or represent high costs to be processed change dynamically
Other definitions of Big Data
McKinsey Global Institute
“Big data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. This definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered big data—i.e., we don’t define big data in terms of being larger than a certain number of terabytes (thousands of gigabytes). We assume that, as technology advances over time, the size of datasets that qualify as big data will also increase. Also note that the definition can vary by sector, depending on what kinds of software tools are commonly available and what sizes of datasets are common in a particular industry. With those caveats, big data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes).
Source: Big data: The next frontier for innovation, competition, and productivity May 2011 Authors: James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Angela Hung Byers.
Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Big data – information of extreme size, diversity and complexity – is everywhere. This disruptive phenomenon is destined to help organizations drive innovation by gaining new and faster insight into their customers. So, what are the business opportunities? And what will they cost?
WIKIPEDIA & THE ECONOMIST
Big data is an all-encompassing term for any collection of data sets so large or complex that it becomes difficult to process them using traditional data processing applications.
The challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, prevent diseases, combat crime and so on.”