You may have heard of the term “big data” being used frequently when discussing data science. But what does that truly entail? What makes big data different from regular data? Well, I’ll be discussing the details of big data to hopefully help you better understand the world of data science.
To begin, big data is simply data that comes on a more grand scale that is difficult to manage with traditional techniques. Big data comes in all forms, such as structured and unstructured, and grows rapidly. So, big data is unique to average data in the way that it can only be worked with using data science. Overall, big data is data of a complex form that comes in large quantities and culminates quickly.
A universal example to wrap your head around big data is Amazon. Amazon as a business collects incredible amounts of data on its customers. Their shopping patterns, where they’re shopping, what they're searching for, and more. They use data science to analyze those patterns so that they can serve up recommendations for their users that can make the customer’s experience better. Furthermore, Amazon has over 300 million users all over the world that they’re keeping up with. Uber and Netflix are also super common examples of big data. Uber compiles all of the statistics on all of their users. That includes locations of users and drivers, ratings, and even driver availability. In terms of Netflix, they are keeping track of the millions of searches every day, shows and movies watched, where things are trending, and so on.
Overall, big data can be encapsulated by five words: volume, velocity, variety, veracity, and value. You can use those five words to remember what big data represents. Those five words can remind you of the large amounts of data involved, how quickly the amount of data increases, the diversity of the data, its accuracy, and its importance. All in all, big data is an integral part of data science and surrounds everyone, every day.
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