4V’s of Big Data: Everything You Need To Know
Have a look at the devices you own. In totality, there must be over a terabyte of media, files, and documents over all the devices. Then, there are millions and millions of such devices. If such a volume of data was not enough, then there are supercomputers, data centers, and huge servers all across the world. […]READ MORE >>
Have a look at the devices you own. In totality, there must be over a terabyte of media, files, and documents over all the devices. Then, there are millions and millions of such devices. If such a volume of data was not enough, then there are supercomputers, data centers, and huge servers all across the world. The overall data currently produced in the world each day is so large that it may require a supercomputer on its own to process it. Such characteristics of big data which is critical for insight generation is dictated by the four V’s.
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4V’s of Big Data
As the name suggests, Big Data should be big in terms of sheer volume. The total amount of information generated each day is growing exponentially. Some experts state that the amount of data created in the last two years is more than what has been generated before that throughout human history. It is also estimated that 2.3 trillion gigabytes of data are generated each day. Additionally, each multinational company will have at least 100,000 GB of data stored.
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It’s not the sheer volume of data that is impressive about big data; it’s the endless variety of it. The diversity originates not only regarding devices or sources of big data generation but also the type of data, including structured and unstructured data. Data is generated via fitness trackers, laptops, smartphones, tablets, supercomputers, and many other mediums. In terms of sources, one of the most significant sources is social media with Facebook, Twitter, and Instagram generating more data than any other communication tool. Today, data scientists are more curious about unstructured data, which can be in the form of voice recording, social media comments, or media files. Using natural language processing and machine learning techniques, data scientists are able to dwell deep into customer behavior.
Apart from the volume of big data, the frequency of incoming data is also increasing each day. For instance, numerous reports published on what happens in an internet second state mind-boggling numbers. In an internet second, more than 50,000 Google searches are performed, 7,000 tweets are sent out, more than 125,000 YouTube videos are viewed, and more than 2 million emails are sent. The flow of big data is massive and continuous, which can help researchers and businesses make valuable decisions.
The sheer volume of big data being generated can pose one major concern for data analysts. Can the data be trusted? The trustworthiness of the big data depends on whether the data is representative, without discrepancies, and suppresses biases. The challenge is also to store and mine the information that is meaningful to the problem being analyzed. When dealing with big data, it is important to keep the data clean and avoid unwanted data from accumulating in your systems. This is mainly because when redundant data is taken into consideration, the resulting insights may be futile.
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