When you learn about big data, it’s usually explained in the context of the three V’s: Volume, Velocity, and Variety. This is a good place to begin understanding big data, but it’s far from the end of the road. Today we’re going to provide a brief overview of the three V’s before explaining how the true value of big data can come from analytics.
Since big data, as a concept, is rather abstract, let’s think in terms that everyone can understand: your smartphone.
When you take a picture, record a video, or even send a text message, this information is stored on your phone as data. The average storage capacity of our phones is increasing with every passing year, but so too is the amount of data we are storing. Not only are we taking more pictures and videos than ever before, but higher resolution cameras mean that the individual file sizes are getting larger as well.
Fortunately, there are cloud storage solutions that offer (nearly) unlimited storage options. With a system in place that transfers data from our phones to the cloud, the storage capacity of our phones is no longer of such importance. Our own phones are, in a sense, now engaged in the collection of big data.
But what if, one day, you decide you want to show someone a funny picture that you took last year. You can’t remember when it was taken or where you were exactly. But it’s important to you that you find this picture. As you scroll through the endless stream of photos on your cloud storage, you begin to question why you take so many photos.
You are coming face to face with the first challenge of big data: the more data you collect, the more difficult it is to organize.
With cloud storage, it’s still a challenge to organize your data, but at least the issue of storing your data is solved. Confident in the cloud, you feel free to take 4K videos of your dog sniffing another dog’s butt.
However, when your phone tells you this video will take an hour and a half to upload, you realize you are faced with a new problem: data velocity.
It’s not enough to be capable of storing endless amounts of data. You need to be able to store it quickly and access it again just as fast.
Speed is a competitive differentiator, especially in the world of information technology. We live in an era where anything less than real-time is considered too slow.
Meet the second challenge of big data: the more data you collect, the more difficult it is to access it quickly.
If all you did with your phone was send text messages, storing the data would be straight-forward enough. Without much difficulty, you could figure out the different pieces of information that need to be stored. Perhaps a method as simple as:
Date | Phone Number | Sender ID | Message
But what happens when you are storing a photograph? Or a video?
Each new data source requires a new mode of thinking about how this information can be stored, organized, and eventually retrieved by the user.
Google’s Cloud system is getting better at solving this problem through advanced image recognition technology. For instance, I can search “soccer” into my Google Image and instantly see pictures I took three years ago at a soccer game. Soccer balls are not visible in the images, and neither is the word “soccer”, but Google’s recognition technology is smart enough to make the connection.
Though despite rapid advances in this technology, Google still needs a hint if it’s going to find that funny picture you took way back when.
The third problem of big data: the larger the variety of data, the more difficult it is to organize and store.
Big Data Problems Need Big Solutions
The three V’s do a good job explaining the different sorts of data, and the challenges involved with storing enormous sets of data. But solving these challenges means moving beyond them.
Stephen Swoyer’s article “big data — Why the 3V’s Just Don’t Make Sense” presents the case for a fourth V: value. I think Swoyer raises a good point. If we’re going to talk about big data as being a game changer, it’s not enough to focus on the different dimensions of the data. We need to focus on what can be done with the data, and how it can benefit our businesses, our customers, and our lives.
It may seem more valuable to have your phone full of data, but if you’re too intimidated by the sheer volume of it to even find the information you need, then what’s the point?
The Value of Big Data Analytics
There’s an enormous opportunity for analytics companies to harness big data to offer the insights that businesses need. This is where the “value” V arises — in the ability to interpret and use your data to effectively make decisions.
At a macro level, you’ve probably experienced this for years with your smartphone’s autocomplete function. If you’ve typed “U-N-D-E” ten times before, and each time your final word has been “understanding”, your phone is likely to suggest “understanding”. That is unless your last name is “Underwood”, then it will likely offer both suggestions, or wait for more information.
Consider the big picture and imagine the potential big data has to disrupt global markets, payment analytics, and so much more. We’re talking about a set of data where Variety no longer measures “text, picture, or video”, but the various interests of potentially every person on earth.
Now that’s big data.
In the age of information, we are rarely hungry for more data. The data is there, an endless buffet of it. The challenge today is to decide what constitutes a balanced diet. To shift through enormous amounts of information, so that all you get on your plate are the insights you need to run your business.
Now that’s big data analytics.