Characteristics Of Big Data - 4 Vs Of Big Data That Can Differentiate Big Data From Traditional Data

Big data, Internet of things, Artificial Intelligence, Virtual Reality and Augmented Reality; do you know what all these terms have in common? They all are the latest trends in technology and have become the buzzwords. But, did you really know what is big data and how it is different from the traditional data? Did you know what the future of big data looks like?

In this article, you will learn about four key characteristics of big data that differentiates it from traditional data.

How Big is Big Data?

To answer this question, here are some of the statsitcs. By 2020, every person will be creating 1.7 megabytes of data every second. By 2020, there would be more than 50 billion connected devices which are capable of creating, analyzing, and sharing the data. To add weight to these claims, here is what technology experts have to say about the big data.

Former CEO of Google, Eric Schmidt once said, “There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days.” Chris Lynch sums it up beautifully when he said, “Big data is at the foundation of all the megatrends that are happening today, from social to mobile to cloud to gaming.”

Ginni Rometty, CEO of IBM, thinks that big data will transform the marketing. Here is how -“Big Data will spell the death of customer segmentation and force the marketer to understand each customer as an individual within 18 months or risk being left in the dust.”

This is just one example. Big data has transformed every industry imaginable. Now, you know how big the big data is, let us look at some of the important characteristics that can help you distinguish it from traditional data.

4 Vs of Big Data

There are four characteristics of big data, also known as 4Vs of big data.

  • Volume
  • Variety
  • Velocity
  • Veracity


We create more than 2.5 quintillion bytes of data every day. You might be wondering how many zeros are in there. There are 17 zeros in a quintillion. Yes, you read that right, 17 zeros. That is enough data to fill 10 million blu-ray discs. The volume of big data has increased tremendously in recent years and data managers and data scientists have to use advanced techniques and tools to filter out the irrelevant data and extract the relevant data from big data. Do not use your servers for data dumping because it will only complicate the things for your business.


Not only is the volume of data growing at an exponential rate but also the variety of formats this data is available in makes it a daunting challenge for data managers and data scientists. From different devices to data sources, the variety of data at our disposal these days makes it even harder to analyze it as you cannot treat all the data with the same yardstick. One size does not fit all in this case. You will have to treat each type of data differently in order to manage it efficiently and get useful insights out of it, which can help you in taking right decisions.


The pace at which new data is created is enough to boggle your mind. In order to survive and thrive in today’s fast-moving business world, you not only need to analyze all that data coming your way but do it quickly. That is what velocity refers to in big data. This requires fast data integration and data analysis as well as ability to extract actionable information from huge data sets quickly. The job of data managers is to make sure that data streaming goes hand in hand with data analysis and data review. The more easily your system can break down data into more manageable chunks the easier it to analyze and extract relevant information quickly.


One characteristic of big data that is mostly misunderstood is veracity. To make it easier for you to understand veracity, here is a simple, short, and focused definition. Veracity refers to the trustworthiness of big data. With 90% data being unstructured, it is hard to separate authentic and accurate data from fuzzy and wrong information. Make sure that all your data is based on facts.

Another important part of veracity is clean and consolidated data. With a huge percentage of unstructured data, keeping the data consistent, clean and consolidated might be a daunting challenge for data scientists. Keep an eye on discrepancies in your data so that wrong data does not reach your big data management software. That is where you need a perfect combination of right tools and data managers come into play.

Did this article help you in understanding the characteristics of big data? Did this article help you in differentiating big data from traditional data? Feel free to share your feedback in the comments section below.

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