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What is Big Data? (Part Two): The 4 V’s … Plus Some Jokes

 

Big Data: water wordscape

Big Data: water wordscape (Photo credit: Marius B)

Big data, as we discussed in my last post, can mean one of two things: huge data that can you see from outer space (with the Great Wall of China and my eighteen-square-mile heap of used Yoo-hoo bottles as the best examples of this type of data) and the ability of businesses to assess and understand massive data-sets. In this two-part piece, we are looking at the latter form of big data (the prior form was explored thoroughly in my interview with the chair of the Belgian Chocolate Milk Society).

We previously looked at ideas on the subject from McKinsey & Company, a global consulting firm that conducted international research on big data across five different fields. Today we will broaden our perspective by looking at thoughts from IBM on how to best approach this type of data. (By IBM, I am referring to the longstanding high-tech company, not the Irritable Bowel Movement, a self-advocacy group for those suffering from IBS.)

To review the first installment of this series, the amount and detail of data worldwide is developing and accruing at an amazing, if not alarming, rate. As for business, the better a company can get at utilizing big data to its advantage will determine how well it is able to compete, both currently and in the marketplace of the future (as seen in Walmart’s 100%-hologram-run and clothing-optional 22nd-SuperCentury stores). McKinsey says that, in fact, it won’t be enough as time goes on to limit big data expertise to IT or another department; instead, the effects of big data will be experienced company-wide.

Moving onto IBM, their exposition on big data is conceptualized as “Four V’s” (not to be confused with the legendary 1960s feminist folk group of the same name).

IBM’s Four V’s of Big Data

How much data do we produce each day? If you guessed 2.3 quintillion bytes, you’re getting close. The correct answer: 2.5 quintillion bytes. In fact, 9 out of every 10 pieces of the data we have available now has been generated between 2011 and today. The data comes from sources as diverse as electronic images, Internet sharing websites, environmental monitoring devices, and my court-ordered ankle brace.

To simplify our understanding of big data – and to help us keep up with the Joneses so that we won’t be stuck with a small-data (such as the number “6” written on a napkin) mindset forever – IBM organizes the topic into four words that all start with “V.” As it turns out, “V” is not always for “vendetta” or “vivification” (of puppets, y’ know).

Volume of Big Data: The volume of information on hand varies by industry – with tech, finance, and government organizations at the fore – but some enterprises have collected data in the petabyte range (also a virtual dog biscuit). What can our world do with this far-reaching info?

  • Use the 84 TBs of tweets generated weekly to better gauge consumer opinions
  • Use the 6.7 billion pieces of data drawn from meters weekly to improve energy efficiency.

Velocity of Big Data: The velocity with which a company takes advantage of information flowing through its network will optimize its usability (as with cybercrime and sales floor streaking).

  • Use the 35 million weekly trade incidents to study fraud detection
  • Use the 3.5 billion weekly phone call reports to improve customer satisfaction.

Variety of Big Data: Brainstorm, categorize, and consider the full range of types of big data. With a better sense of how this data interrelates, you will gain a better sense of general vs. specific trends (as with mullets vs. perm mullets).

  • Use hundreds of real-time surveillance video feeds to zone in on specific locales of concern
  • Use the 80% rise in content-based Web data to enhance knowledge of demographic sensibilities.

Veracity of Big Data: A third of corporate decision-makers do not believe the data they are using to make their decisions is reliable. Reliability of the data that comprises big data, then, and providing convincing arguments for its veracity, are huge obstacles to overcome. These hurdles are pronounced as sources become even more manifold.

Conclusion & Continuation

As IBM shows us – and as we learned from the McKinsey comments presented in the previous half of this series – big data is not just a bunch of numbers, words, images, and contexts. Rather, it’s an incredible opportunity for businesses to meet the needs of consumers and to outpace their competition. That finishes us off with our exploration of big data.

Also, please note, if anyone from the City of Pierre is reading this: I have been living underwater for the last seven weeks. That’s why my ankle bracelet says I’m in the river. I didn’t remove it and throw it off the bridge.

And, um, did I mention that at Superb Internet, we are experts on hosting, colocation, and managed support?

By Kent Roberts

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