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Big Data: Choosing ‘Medium’ Instead of ‘Big’ When ‘Right-Sizing’ Your Data

By Dick Weisinger

Big Data is the rage, but the reality now is that most businesses really don’t have data sets that large to be classified as ‘Big Data’.

Gartner analysts Merv Adrian and Nick Huedecker note that Hadoop and Big Data projects have been “slow to grow so far.”

Harper Reed, Chief Technology Officer at Obama for America 2012, said that most businesses “probably have medium data.  Big Data has now come to represent analytics tools, not the data itself.”

Matt Hunt, open source project lead at Bloomberg, said that “what we have is a ‘medium data’ problem — and so does everyone else.   Systems such as Hadoop and Spark are less efficient and mature for these typical low latency enterprise uses in general. High core counts, SSDs, and large RAM footprints are common today – but many of the commodity platforms have yet to take full advantage of them, and challenges remain.”

Tableau calls Medium Data the “New sweet spot.”  It is a way to ‘right size‘ your data analytics efforts.

David Rothschild, an economist with Microsoft Research, is quoted in a piece by  Haniya Rae saying that “people have gotten really excited about big data.  It’s a massive amount of discussion about individual products, but in some ways it’s thinner than we give it credit for.”  Rothschild says that ‘Big Data’ analytics tools can ‘supercharge’ smaller data-source projects.  “Not to say that Big Data isn’t valuable, but medium data proves to be more valuable on a day-to-day basis.”

Tableau notes that ‘medium’ isn’t bad, in fact, with “a bell curve, when you’re in the middle, you’re really on top.”

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