Colleges and universities need to understand why it is important to differentiate between Big Data and analytics.
Big Data, a complex collection of data sets used to analyze information, can be characterized by the degree of complexity within the data set and how much value can be derived from innovative vs. non-innovative analysis techniques.
But just how does Big Data differ from analytics, a multi-faceted discipline of discovery and communication of relevant data patterns?
Andrew McAfee, a principal research scientist at MIT’s Center for Digital Business and Erik Brynjolfsson, director of the MIT Center for Digital Business, attempted to answer this question by arguing that big data differs from analytics in three key ways:
1) Volume – Recent research by the analyst firm International Data Corp. (IDC) indicates that the global amount of digital data will grow from 130 exabytes to 40,000 exabytes by 2020.
For example, Walmart collects more than 2.5 petabytes of data every hour from customer transactions alone. A petabyte is one quadrillion byte or about the equivalent of 20 million cabinets of text. Consider that 90 percent of the data today was created only in the last two years.
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