For those who aren’t aware, “big data” is the term given to the large amounts of data (both structured and unstructured) that businesses must deal with daily. These data sets can be analyzed to help business owners make more accurate decisions and implement new strategies.
From the outside looking in, it would be fair to assume that the more data a company has, the better, as the company in question will have a larger sample size to pull from, and as a result, their data will be more accurate. However, that isn’t always the case, as learning how to handle big data effectively has become a very challenging task for many businesses around the world.
Big data: In a nutshell
Thanks to the internet and the massive amounts of information available online, big data has revolutionized entire industries and made things possible that would have seemed like science fiction just a few decades ago. Nowadays, we can map disease outbreaks in real-time, explore the visible universe, and even use data from wearable devices to aquire instantaneous feedback about our health.
Even companies like Netflix are in on the act, using big data to generate billions of dollars by discovering customer behavior and buying patterns before using that information to recommend movies and TV shows based on their subscribers’ preferences.
Although, it’s important to point out that the term “big data” is relatively new, after only being first coined back in 2005 by Roger Mougalas (shortly after he created the term Web 2.0). Mougalas described big data as so expensive and complex that it is practically impossible to analyze and process using traditional methods.
As time moved on, big data problems have only become more challenging, as we are now producing more data than ever before, and it’s increasing exponentially. So much so that if you burned all of the data created in just one day onto DVDs and stacked them on top of each other, they would be tall enough to reach the moon – twice!
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