Archives For May 28, 2013

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For an article that deems it provocative to question big data, the GigaOm post on big data today has a pretty provocative title of its own.  Provocative enough to have gotten me to read the article.  But, reading this reminded me of some other articles I’ve read that have been littered with buzzwords.  

I see this regularly in tech articles – a roughly related set of jargon that are all conflated, without actually making much sense.  In this particular case, the conflation is among data, databases/data storage, algorithms/data analysis methods and (predictive) models.  To put actual data (e.g., social media data that the article talks about), data computational paradigms such as Hadoop and models all at the same level as if there was some comparison to draw on these is glaringly ignorant.  The writer claims that big data might be more about automation than insights – even provides a link to another article (written by himself) that supports that theory! 

Part of the problem here is of course that the term “big data” has been miserably overloaded.  So much that “data” in that term is only incidental.  It is almost like a discipline now.  When we see articles like this one, it is time for a reset on the terminology.  It is partly because of this type of hype that we often get customers and others thinking that machine learning is “magic”.  And that it involves a black box taking a bunch of data and churning out magical insights.  

To be fair, not all of that article is useless – it makes a couple of valid points, especially around knowing expectations before setting out on a big data project.  It is true that you will get very little out of an exploration of data without objectives.  Knowing what you are looking to understand from the data is key to getting somewhere with it.  

When it comes to terminology, the tech world hasn’t been traditionally strong.  Terms like “big data” and “software defined X” have made it possible to turn anything into a topic related to the hottest buzzword in the industry.  It would be good to see better quality writings from famous professional blogs like GigaOm – but such things are par for the course!  

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