Analytics for Human Information: Top Ten New Myths of Big Data - Myth #1

If you have been following the chatter in our industry surrounding “Big Data” you may have noticed something that came to my attention last week.  Namely, that a lot of the topics and issues that we have all been discussing in regards to Big Data haven’t really changed much in the last year or two.  This would tend to imply that Big Data has become mature as a business and technology phenomenon, and that we’ve all figured out how to integrate Big Data into our organizations’ operations.

 

However, I suspect that you would agree that this is not the case.  Rather, most technology and business people with whom I interact are at least as perplexed today by how to make Big Data work and become useful as they were a couple of years ago; if not more so.  This makes sense, as Big Data is following the same adoption lifecycle as every other technology that has preceded it.

 

I am fairly sure that there was “that guy” 5,000 years ago, who was trying to sell his latest, greatest technology to any clan that he came across; something called “writing.”  He rambled endlessly about its technical merits, about how the clan could run more effectively, more efficiently and with fewer replication errors in their story telling.  He went further to say that all that the clan would have to do is buy a few stone tablets (first generation servers), hire a “carving rock star” (first-generation developer) to set up their symbols (first-generation software) and this new technology would transform their clan.  He even showed them a tablet with some writing on it (first-generation proof of concept), and sheepishly apologized that the chisel didn’t have a “start” button on it. Meanwhile, the clan leader was thinking, “How am I going to afford all of these tablets, and where am I going to store them?” (first-generation CEO).

 

Back to today’s world, Big Data is rapidly advancing through the business world, and the vast majority of companies are at least experimenting with the technology, if not rolling it out for production purposes.  As this migration has occurred, our collective understanding of Big Data has begun to change, propelling a whole new group of “myths” to the forefront.  In this and nine subsequent blog posts, I wanted to introduce these Ten New Myths About Big Data, and provide some food for thought as to why these ten perceptions are inaccurate, if not plainly wrong.

 

Big Data Myth #1: Big Data is the same as other analytics, it’s just bigger

I think that this myth has been propagated by those who think that they understand Big Data to those who know that they do not.  By comparing Big Data to preceding analytic efforts such as Business Intelligence (BI) we have tried to bridge a conceptual gap that is far larger than this description can possibly close.  Big Data is not “BI only bigger,” it is a whole new approach to digesting entirely new collections of data. 

 

Big Data is a process where we are joining data sets that have never been joined before, digesting them in ways that we were never able to digest before and thereby answering questions that we never believed were ask-able before.  In fact, if these three goals are not part of your Big Data strategy you’re not really doing Big Data.

 

For those of us at HP working this issue, the most fundamental difference between Big Data and prior analytic efforts is the merging of structured data and unstructured data. Three score and seven years after the ENIAC computer was first plugged in, our industry is very comfortable with the collection, analysis and management of structured transactional data.  However, this structured information has recently been completely overtaken by the generation of unstructured data; stuff like video, audio, email, tweets and so on.  This unstructured information is extraordinarily difficult for computers to process, let alone comprehend.  And yet, it is this same information that is rich in context and meaning and is the best path towards allowing organizations to better serve their customers. 

 

In architecting our new information platform HAVEn we have tried to remain laser-focused upon the need to merge vast quantities of structured transactional data with equally-vast amounts of unstructured, human information.  Combining the unique capabilities of Hadoop, Vertica and Autonomy has created a platform that allows businesses to consume this vast ocean of data and transform it into cupful’s of knowledge that the business can actually act upon.

 

This last point is critical, as I doubt many companies are investing in Big Data analysis because to do so is cool, and executives like how the word ‘petabyte’ rolls off of the tongue. Rather, they are making these investments because they realize that the entire future success of their business depends upon making Big Data work. At least I hope that they realize this, because I certainly believe it to be so.

 

Click below to continue reading The New Top Ten Myths of Big Data blog series:

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About the Author
Chris Surdak is a Subject Matter Expert on Information Governance, analytics and eDiscovery for HP Autonomy. He has over 20 years of consul...


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