Tag Archives: McKinsey & Company

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

What is Big Data? (Part One): 7 Findings … Plus Some Jokes

 

Big Data

Big data can mean a number of different things. As we all know, it can mean writing data in a large, thick marker or painting pieces of data on massive canvases for an epic-scale art project in Central Park (which is technically “enormous data”). Big data, though, can also refer to the process of analyzing large sets of data: we’ll explore that type below.

To conduct this mission, let’s look into the viewpoints of the global consultancy McKinsey & Company as well as those of IBM (the latter of which is in no way associated with IBS, aka spastic colon, at least not publicly).

The gist of our effort today is to gather a basic perspective on how the exponentially increasing volume of data is changing business and the world in general. As time goes on, businesses will need to stay in-step with developments in big data if they hope to keep up with other companies (such as full-spectrum and complex information pertaining to Justin Bieber’s earwax accrual and maintenance). In fact, per McKinsey, it won’t cut it to have expertise limited to a specific department of a company. Rather, everyone in business will be affected by this mind-boggling explosion of information.

I’ll focus on Big Data (or is it BIG DATA?) in a two-part mini-series, not to be mistaken with the two-part mini-series on the Big Bird road rage scandal produced by TMZ. This first part will focus on the research by McKinsey into the subject; while the second part will focus on IBM’s perspective.

McKinsey’s 5 Points of Focus

In order to get as broad a sense as possible of big data, the McKinsey Global Institute looked at the subject in five different areas (six if you count the Earth’s parallel reality that is void of all words, shapes, sounds, and descriptions):

  • US Healthcare & Medicine
  • European Government
  • US Retail Sales (including, but not limited to, bagged kidney beans)
  • Worldwide Manufacturing (except for kidney bean bags)
  • Individual GPS Services

 

The value available via understanding big data is impossible to ignore. As an example of the impact expertise in big data can have on business, McKinsey found that retailers optimized to incorporate large data sets into their business can boost their operational margin in excess of 60%.

McKinsey’s 7 Key Findings

McKinsey organized its report on the study into seven major findings.  Let’s look at each one below:

  1. Just how big is big data? McKinsey believes that, as of 2009, the average amount of data held within the network of companies of a thousand employees or more was “at least … 200 terabytes.” That’s double what Walmart had at its fingertips just ten years earlier (though that yellow smiley-face thing “stole” 78% of the company’s information when he was terminated: it was in his head).
  2. How is big data valuable? McKinsey says there are five basic ways to understand the value of this type of data: a.) data clarity and implementation at a high rate of speed; b.) flexibility based on a better grasp of fluctuations; c.) more meaningful and directed customer segments; d.) better reliability with decisions; e.) more complex ability to innovate. f.) there is no “f.”
  3. How is it a competitive advantage? Big data is anything but simple. The levels of depth and ability of companies to proactively use data efficiently – even in real time (and unreal time) – will be a major factor moving forward in business success.
  4. How does big data enhance consumer surplus and company productivity? The implications for personal economic efficiency and business streamlining will be enormous. As mentioned above, retailers will benefit by greatly improving their operational margins; consumers, meanwhile, can already benefit from a surplus of $600 billion due to the effectiveness of GPS tracking technology (such as the 17 devices sticking into my brain, complete with external antennae).
  5. Which industries will fare best? Certain types of businesses will be more positively impacted by an increasing sense of how to utilize big data. Victors will be those businesses directly intertwined with digital technology (such as companies building the Internet or anything with a bunch of cords), the banking industry, and the public sector.
  6. Who will specialize in big data? Within 5 years, the McKinsey report suggests, the US will experience a dearth of specialists in this field, to the tune of almost 1.7 million (while the world as whole will be impacted to the tune of Leonard Cohen’s “Suzanne”).
  7. How to realize the strongest results? In order to take big data to the next level, companies will need to reconsider and address issues of privacy and security, alongside the legal ramifications of the flow of information. Additionally, integration of more than one stream of data into central systems will allow better administration and monitoring. Infrastructure must meet big data’s needs, such as sensual reproductive ones, as well.

Conclusion & Continuation

As you can see, big data is growing in complexity, and what it means to business will be a challenge wise companies will want to meet head-on and early. McKinsey’s research findings make this all too obvious. However, so we get a broader perspective on the topic, let’s look at IBM’s statements as well, in Part Two. Then we’ll look at tiny, disembodied data, such as this number: 4.

By Kent Roberts