To continue the discussion from our previous blog, “Perspectives on Big Data”, we can ascribe a few more characteristics to the term. Big Data is often incomplete, can be very hard to access, often has few easily definable relationships with other pieces of data, and its relevance may well be time sensitive.
According to the “Webopedia Computer Dictionary”, the term Big Data is “believed to have originated with Web search companies who had to query large distributed aggregations of loosely-structured data.” It suggests as an example, “trillions of records of millions of people — all from different sources (e.g. Web, sales, customer contact center, social media, mobile data, and so on).”
There are some who suggest that this proliferation of Big Data definitions makes the term “effectively meaningless”. The quote is from Steve Duplessie of “Enterprise Strategy Group”, although he then goes on to define Big Data himself with some now familiar terms: “any dataset that breaks the boundaries and capabilities of IT”, and “More than just data volume, smart strategies also consider velocity, variety, and complexity of information”. While the first encounters of Big Data may have come from Transaction processing systems, he points out that the data now comes from “social media” in the form of “video, audio, etc.” and from “every known device on the planet”. But, he also points out that, even when talking about more traditional data sources like “transaction processing, reporting and analytics, backup and disaster recovery, and application testing and development, and other copies”, such as replications to populate data warehousing systems, you can see the potential for one piece of data (datum) to “grow 100-fold”.
So, this is a phenomenon that has been building for a long time. In future blogs, we will discuss how people are tackling this problem.
Link to article mentioned: http://www.informationweek.com/news/global-cio/interviews/232301375
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I enjoyed your blog – excellent effort!
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