SEP 21, 2012 8:00am ET

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The big data phenomenon has given rise to a lot of discussion. I think that there is general consensus that big data is something more than just ultra-large scale data at the petabyte level. It is somehow different.

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Comments (2)
Thanks for a great article! I enjoyed reading it. However, you picked a bad example regarding the speed of light. From Wikipedia: The speed of light in vacuum, commonly denoted c, is a universal physical constant important in many areas of physics. Its value is 299,792,458 metres per second, a figure that is exact because the length of the metre is defined from this constant and the international standard for time.
Posted by W C | Friday, September 21 2012 at 11:35AM ET
Interesting insights, and I am refreshed that you have started to pick the concept of Big Data apart. The Four V's is too big a basket.

However, even on the "observational" side of data, there are differences of essence. The notion of scientific observation assumes many things, including that the observer be careful, competent, trustworthy, etc. However, much of what makes up the bulk of Big Data in the corporate marketing space, which is to say social media (c.f. avionics and other sensor and control data) is pure opinion. Or sometimes even less than that: raw emotion. Sometimes it is intentionally defamatory, or even downright libelous. But it is still valuable, and companies and organizations are trying to understand it. Generally each datum is considered untrustworthy, and it is only the statistically significant trends that are considered valuable. Though in reality, this is not that different from many scientific endeavors like drug clinical trials, where it is likewise the trends within many observations that are most important.

so, is there are third category, the data of Opinion, Conjecture and/or Ulterior Motive?

Of course, when it comes to form, social media content, along with anything in textual form, is totally different for database data. For one thing, it is completely free of a formal schema, apart from the "rules" of syntax of the language in which it's written. For example, "The length of the bolt is 2 inches, while it is a half inch in diameter." expresses a couple of concepts that are very precise and free from opinion and bias (though they could still be factually incorrect...) and that are easily structured:

length: 2.0: diameter: 0.5"

However, the structure of the original is embedded in the syntactic and semantic content of the sentence.

Furthermore, the "rules" of language are often broken, which is part of what makes something like a Twitter tweet so much harder to parse and model than a WSJ article.

Anyway, it should be clear from this (to a human reader anyway) that my opinion is that Text Analytics is one of the biggest challenges within the Big Data sphere. But try getting a computer program to come to that conclusion...

Posted by Chris R | Friday, September 21 2012 at 1:27PM ET
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