The 5vs Of Big Data: The Characteristics Of A Mass Of Data

The 5vs Of Big Data: The Characteristics Of A Mass Of Data

All the big digital giants make use of big data. But how is a mass of data defined? We need to consider the 5vs of big data.

Before going into detail, let’s start by saying that we are talking about volume, speed, variety, truthfulness, and value. These are the five main characteristics identified by the great giants to define the system for cataloging and storing all the data obtained through users.

The very name that defines them, big data, indicates a large data size. When there is a lot of data, it becomes essential to manage them wisely.

This huge amount of information cannot be handled with conventional methods as it is gathered not only by various mechanisms, including software, sensors, devices, and other hardware, but also grow over the years as computing power becomes more affordable and accessible. Manual management of these amounts of data is unthinkable.

In this article, we examine which data can be part of the big data category, what components determine the acquisition speeds, what different natures they can have, what is the level of the truthfulness of the data, and last but most important, what is the They. Finally, let’s see why knowing them is important for any company.

Let’s find out more about the 5vs of big data:

Volume Of Big Data

It is estimated that by the end of this year, we will have nearly 40,000 ExaBytes of data.

Tech greats like Amazon get real-time data every second from millions of users. They perform near real-time data processing, and after running, machine learning algorithms make decisions to deliver the best customer experience.


By speed, on the other hand, we mean the speed in accumulating data. Data flows from sources such as cars, networks, social media, mobile phones, etc.

The flow is constant and continuous and determines the potential concerning the rate at which data is generated and processed to satisfy requests.

Sampling data can help in addressing problems such as “speed.”

Example: More than 3.5 billion searches are performed on Google per day. In addition, Facebook users are increasing by 22% (approximately) year on year.

Let’s always consider a social network platform and imagine billions of people uploading various types of content such as photos, videos, and texts daily, at all hours. The speed with which this data is transferred from user devices to the company’s servers defines speed itself.

We, therefore, speak of data transfer speed.


The latter refers to the nature of the data:

  • Structured: is organized data and refers to information that has defined the length and format of the data.
  • Semi-structured: they are semi-organized data, and it is a form that does not conform to the formal structure of the data. 
  • Unstructured: this is data that is not organized and therefore does not fit perfectly into the relational database’s traditional structure of rows and columns. Texts, images, videos, etc. They are examples of unstructured data that cannot be stored in the form of rows and columns.

The data of the real world are not homogeneous, and it is difficult for those who have to manage them and catalog them in an orderly manner. Videos, photos, captions, comments, and hyperlinks, all linked together, can represent a real challenge for those involved in designing and analyzing data.


Organizing data does not always (indeed, we could say almost never) turns out to be a simple operation since inconsistencies, redundancies, and inconsistencies in the management of the data are created.

It means that the data can get messy due to the variables described above and are consequently very difficult to control.

The masses of data are also very variable and dynamic and come from different sources, creating confusion. In a world of such heterogeneous data, it is difficult to determine what is right and wrong. The truthfulness, therefore, indicates the level of reliability or unreliability of the data.


The last of the fundamental variables of big data is the one that defines the value. The latter is a fundamental variable because the importance of big data itself lies in the possibility of being useful for companies and, therefore, bringing benefits.

In fact, the data for its own sake has no importance. To be truly useful, they must be able to be converted into valuable information that allows companies to verify and possibly modify their moves.

If we take Netflix, for example, we realize this is extremely true. Just think of the amount of information that every minute can obtain from its users and how user behavior affects business decisions by determining how the company moves concerning creating new content or eliminating others.

The data is also helpful for the users themselves, who, based on their viewing preferences, receive advice on other content that may be of interest to them.

The use of big data for Netflix means reducing abandonment for users.

Those who use information from big data gain a competitive advantage.

Why Is Knowing The 5vs Of Big Data Important For Companies?

When we think of big data, the big companies that produce them immediately come to mind. We are a little less inclined to think about the use that small and medium-sized enterprises can make of it.

It should also be considered that modest amounts of data can be produced by corporate assets, such as the company’s website.

Being able to read and, above all, interpret the data can help entrepreneurs, employees, and managers to better understand what is happening in the various aspects of the company. They can also help them get to know their customers better and offer them what they want most.

Knowing the 5vs of big data is essential to understanding how to exploit them to your advantage.

Also Read : Innovation Is The Heart Of Corporate Sustainability


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