Big Four Vs of Big Data

When dealing with big data, it is vital to keep the data clean and avoid dirty data from accumulating in your systems

Quantzig, a global analytics solutions provider, has announced the completion of their latest article on the big four Vs of Big Data.

Big Data has important characteristics and properties that can help you understand both the challenges and advantages of initiatives. In all the devices we own, there must be over a terabyte of media, files, and documents. Then, there are millions and millions of such devices.

Apart from such devices, there are supercomputers, data centers, and huge servers all across the world. The overall data produced in the world each day is so huge that it may need a supercomputer on its own to process it. Such features of big data which is very important for insight generation is dictated by the four Vs.

According to the analytics experts at Quantzig, “When dealing with big data, it is vital to keep the data clean and avoid dirty data from accumulating in your systems.”

Volume: Big data is always large in terms of volume. The overall amount of information produced each day is rising exponentially. Some experts have predicted that the amount of data generated in the last two years is more than what has been created before that throughout human history. It is also projected that 2.3 trillion gigabytes of data are generated each day.

Variety: The endless variety of data is more impressive than its sheer volume. The diversity is not only regarding devices or sources of data generation but also the type of data, along with structured and unstructured data. Data is generated through laptops, fitness trackers, tablets, smartphones, supercomputers, and many other mediums.

One of the most substantial sources is social media platforms with Twitter, Facebook, and Instagram producing more data than any other communication tools. At present, data scientists are more inquisitive about unstructured data, which can be in the form of social media comment, voice recording, or media files. Using machine learning techniques and natural language processing, data scientists can understand customer behavior.

Velocity: The frequency of incoming data is also increasing each day. For example, many reports published on what happens in an internet second show overwhelming numbers. In an internet second, more than 50,000 Google searches are completed, more than 125,000 YouTube videos are viewed, 7,000 tweets are sent out, and more than 2 million emails are sent. The flow of data is huge and constant, which can help researchers and companies to make valuable decisions.

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